Method for training and testing obfuscation network capable of processing data to be obfuscated for privacy, and training device and testing device using the same

ABSTRACT

A method for training an obfuscation network for obfuscating data is provided. The method includes steps of: a learning device (a) (i) inputting training data into an obfuscation network to obfuscate the training data and generate obfuscated training data, and (ii) inputting the obfuscated training data into a compression network to generate binary training data and generate compression adaptive obfuscated training data; (b) inputting the compression adaptive obfuscated training data into a learning network to apply learning operation and generate first characteristic information and inputting the training data into the learning network to generate second characteristic information and (c) training the obfuscation network such that first errors, calculated using the first and the second characteristic information, are minimized and such that second errors, calculated using (1) modified training data or modified obfuscated training data and (2) the obfuscated training data or the compression adaptive obfuscated training data, are maximized.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit and priority of Korean ApplicationNo. KR 10-2020-0137122, filed on Oct. 21, 2020, the entire disclosure ofwhich is incorporated herein by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates to a method for training an obfuscationnetwork capable of obfuscating, e.g., anonymizing or concealing,original data to protect personal information and a learning deviceusing the same, and to a method for testing the trained obfuscationnetwork capable of obfuscating the original data to protect the personalinformation and a testing device using the same.

BACKGROUND OF THE DISCLOSURE

Big data refers to data including all of unstructured data andsemi-structured data not utilized so far, like e-commerce data,metadata, web log data, radio frequency identification (RFID) data,sensor network data, social network data, data of Internet text anddocuments, Internet search indexing data, as well as all of structureddata used by conventional enterprises or public institutions. Data assuch is referred to as the big data in the sense that common softwaretools and computer systems cannot easily handle such a huge volume ofdata.

And, although such a big data may have no meaning by itself, it can beuseful for generation of new data, judgment or prediction in variousfields through machine learning on patterns and the like.

Recently, due to the strengthening of a personal information protectionact, it is required to delete information, that can be used foridentifying individuals, from the data or to acquire consent of theindividuals in order to trade or share such a big data. However, it isnot easy to check if any information that can be used for identifyingthe individuals is present in such a large amount of the big data, andit is impossible to obtain the consent of every individual. Therefore,various techniques for such purposes have emerged.

As an example of a related prior art, a technique is disclosed in KoreanPatent Registration No. 1861520. According to this technique, aface-concealing method, e.g., a face-anonymizing method, is providedwhich includes a detection step of detecting a facial region of a personin an input image to be transformed, a first concealing step oftransforming the detected facial region into a distorted first imagethat does not have a facial shape of the person so that the person inthe input image is prevented from being identified, and a secondconcealing step of generating a second image having a predeterminedfacial shape based on the first image, transforming the first image intothe second image, where the second image is generated to have a facialshape different from that of the facial region detected in the detectionstep.

However, according to conventional techniques as well as the techniquedescribed above, it is determined whether identification informationsuch as faces, text, etc. is included in the data, and then a portioncorresponding to the identification information is masked or blurred. Asa result, a machine learning algorithm cannot utilize such data due todistortion of original data. Also, in some cases, the data may containunexpected identification information which cannot be concealed, e.g.,anonymized. In particular, a conventional security camera performs ananonymizing process by blurring every pixel changed between frames dueto a target to be anonymized moving between the frames in a video, andif the anonymizing process is performed in this manner, criticalinformation such as facial expression of an anonymized face becomesdifferent from information contained in an original video, and also,personal identification information overlooked during face detection mayremain on the original video.

Accordingly, the applicant of the present disclosure proposes a methodfor generating obfuscated data by obfuscating the original data suchthat the obfuscated data is different from the original data, while aresult of inputting the original data into a learning model and a resultof inputting the obfuscated data into the learning model are same as orsimilar to each other.

SUMMARY OF THE DISCLOSURE

It is an object of the present disclosure to solve all theaforementioned problems.

It is another object of the present disclosure to perform obfuscation,e.g., anonymization or concealment, in a simple and accurate way, byeliminating processes of searching general data for personalidentification information and processes of obfuscating, e.g.,anonymizing or concealing, the personal identification information.

It is still another object of the present disclosure to protect privacyand security of original data by generating obfuscated data, e.g.,anonymized data or concealed data, through irreversibly obfuscating theoriginal data.

It is still yet another object of the present disclosure to generateobfuscated data recognized as similar or same by computers, butrecognized as different by humans.

It is still yet another object of the present disclosure to stimulate abig data trade market.

In order to accomplish the objects above, distinctive structures of thepresent disclosure are described as follows.

In accordance with one aspect of the present disclosure, there isprovided a method for training an obfuscation network to be used forobfuscating original data to protect personal information, includingsteps of: (a) if training data is acquired, a learning device performingor supporting another device to perform (i) a process of inputting thetraining data into an obfuscation network, to thereby allow theobfuscation network to obfuscate the training data and thus to generateobfuscated training data, and (ii) a process of inputting the obfuscatedtraining data into a compression network, to thereby allow thecompression network to (ii-1) compress the obfuscated training data andthus generate binary training data and (ii-2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata having data compression information; (b) the learning deviceperforming or supporting another device to perform (i) a process ofinputting the compression adaptive obfuscated training data into alearning network having one or more learned parameters, to thereby allowthe learning network to (i-1) apply a learning operation to thecompression adaptive obfuscated training data by using the learnedparameters and thus (i-2) generate first characteristic information fortraining corresponding to the compression adaptive obfuscated trainingdata and (ii) a process of inputting the training data into the learningnetwork, to thereby allow the learning network to (ii-1) apply thelearning operation to the training data by using the learned parametersand thus (ii-2) generate second characteristic information for trainingcorresponding to the training data; and (c) the learning deviceperforming or supporting another device to perform a process of trainingthe obfuscation network such that (i) at least one first error,calculated by referring to the first characteristic information fortraining and the second characteristic information for training, isminimized and (ii) at least one second error, calculated by referring to(ii-1) (ii-1-a) modified training data, created by modifying thetraining data, or modified obfuscated training data, created bymodifying the obfuscated training data, and (ii-1-b) the obfuscatedtraining data or (ii-2) (ii-2-a) the modified training data or themodified obfuscated training data and (ii-2-b) the compression adaptiveobfuscated training data, is maximized.

As one example, the learning network includes a first learning networkto an n-th learning network respectively having one or more firstlearned parameters to one or more n-th learned parameters wherein n isan integer greater than 0, wherein, at the step of (b), the learningdevice performs or supports another device to perform (i) a process ofinputting the compression adaptive obfuscated training data into each ofthe first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into each of the first learning network to the n-th learningnetwork, to thereby allow each of the first learning network to the n-thlearning network to (ii-1) apply its corresponding learning operation tothe training data by using respectively the first learned parameters tothe n-th learned parameters, and thus (ii-2) output each piece of(2_1)-st characteristic information for training to (2_n)-thcharacteristic information for training on the training data, andwherein, at the step of (c), the learning device performs or supportsanother device to perform a process of training the obfuscation networksuch that the first error, which is an average over (1) a (1_1)-st errorcalculated by referring to the (1_1)-st characteristic information fortraining and the (2_1)-st characteristic information for training to (2)a (1_n)-th error calculated by referring to the (1_n)-th characteristicinformation for training and the (2_n)-th characteristic information fortraining, is minimized and such that the second error is maximized.

As one example, the learning network includes a first learning networkto an n-th learning network respectively having one or more firstlearned parameters to one or more n-th learned parameters wherein n isan integer greater than 0, wherein, at the step of (a), the learningdevice performs or supports another device to perform (i) a process ofinputting the training data into the obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate first obfuscated training data and (ii) a process of inputtingthe first obfuscated training data into the compression network, tothereby allow the compression network to (ii-1) compress the firstobfuscated training data and thus generate first binary training dataand (ii-2) decompress the first binary training data and thus generatefirst compression adaptive obfuscated training data having the datacompression information, wherein, at the step of (b), the learningdevice performs or supports another device to perform (i) a process ofinputting the first compression adaptive obfuscated training data intothe first learning network, to thereby allow the first learning networkto (i-1) apply the learning operation to the first compression adaptiveobfuscated training data by using the first learned parameters of thefirst learning network and thus (i-2) output (1_1)-st characteristicinformation for training on the first compression adaptive obfuscatedtraining data and (ii) a process of inputting the training data into thefirst learning network, to thereby allow the first learning network to(ii-1) apply the learning operation to the training data by using thefirst learned parameters and thus (ii-2) output (2_1)-st characteristicinformation for training on the training data, wherein, at the step of(c), the learning device performs or supports another device to performa process of training the obfuscation network such that (i) at least one(1_1)-st error, calculated by referring to the (1_1)-st characteristicinformation for training and the (2_1)-st characteristic information fortraining, is minimized and (ii) at least one (2_1)-st error, calculatedby referring to (ii-1) (ii-1-a) the modified training data or firstmodified obfuscated training data created by modifying the firstobfuscated training data and (ii-1-b) the first obfuscated training dataor (ii-2) (ii-2-a) the modified training data or the first modifiedobfuscated training data and (ii-2-b) the first compression adaptiveobfuscated training data, is maximized, to thereby allow the obfuscationnetwork to be a first trained obfuscation network, and wherein, whileincreasing an integer k from 2 to n, the learning device performs orsupports another device to perform (i) a process of inputting thetraining data into the (k-1)-th trained obfuscation network, to therebyallow the (k-1)-th trained obfuscation network to obfuscate the trainingdata and thus to generate k-th obfuscated training data and a process ofinputting the k-th obfuscated training data into the compressionnetwork, to thereby allow the compression network to (1) compress thek-th obfuscated training data and thus generate k-th binary trainingdata and (2) decompress the k-th binary training data and thus generatek-th compression adaptive obfuscated training data having the datacompression information, (ii) (ii-1) a process of inputting the k-thcompression adaptive obfuscated training data into a k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the k-th compression adaptive obfuscated trainingdata by using one or more k-th learned parameters of the k-th learningnetwork and thus to output (1_k)-th characteristic information fortraining on the k-th compression adaptive obfuscated training data and(ii-2) a process of inputting the training data into the k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the training data by using the k-th learnedparameters and thus to output (2_k)-th characteristic information fortraining on the training data, and (iii) a process of training the(k-1)-th trained obfuscation network such that at least one (1_k)-therror, calculated by referring to the (1_k)-th characteristicinformation for training and the (2_k)-th characteristic information fortraining, is minimized and such that at least one (2_k)-th error, whichis calculated by referring to (iii-1) (iii-1-a) the modified trainingdata or k-th modified obfuscated training data calculated by modifyingthe k-th obfuscated training data and (iii-1-b) the k-th obfuscatedtraining data or (iii-2) (iii-2-a) the modified training data or thek-th modified obfuscated training data and (iii-2-b) the k-thcompression adaptive obfuscated training data, is maximized, to therebyallow the (k-1)-th trained obfuscation network to be a k-th trainedobfuscation network.

As one example, at the step of (c), on condition that an obfuscatedtraining data score, corresponding to the obfuscated training datainputted into a discriminator capable of determining whether itsinputted data is real or fake or the compression adaptive obfuscatedtraining data inputted into the discriminator, has been acquired as thesecond error, the learning device performs or supports another device toperform a process of training the obfuscation network such that thefirst error is minimized and the second error is maximized and a processof training the discriminator such that a training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized.

As one example, the learning network includes a first learning networkto an n-th learning network respectively having one or more firstlearned parameters to one or more n-th learned parameters wherein n isan integer greater than 0, wherein, at the step of (b), the learningdevice performs or supports another device to perform (i) a process ofinputting the compression adaptive obfuscated training data into each ofthe first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into each of the first learning network to the n-th learningnetwork, to thereby allow each of the first learning network to the n-thlearning network to (ii-1) apply its corresponding learning operation tothe training data by using respectively the first learned parameters tothe n-th learned parameters, and thus (ii-2) output each piece of(2_1)-st characteristic information for training to (2_n)-thcharacteristic information for training on the training data, andwherein, at the step of (c), the learning device performs or supportsanother device to perform (i) a process of training the obfuscationnetwork such that the first error, which is an average over (i-1) atleast one (1_1)-st error calculated by referring to the (1_1)-stcharacteristic information for training and the (2_1)-st characteristicinformation for training to (i-2) at least one (1_n)-th error calculatedby referring to the (1_n)-th characteristic information for training andthe (2_n)-th characteristic information for training, is minimized andsuch that the second error, which is the obfuscated training data score,corresponding to the obfuscated training data inputted into thediscriminator or the compression adaptive obfuscated training datainputted into the discriminator, is maximized and (ii) a process oftraining the discriminator such that the training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized.

As one example, the learning network includes a first learning networkto an n-th learning network respectively having one or more firstlearned parameters to one or more n-th learned parameters wherein n isan integer greater than 0, wherein, at the step of (a), the learningdevice performs or supports another device to perform (i) a process ofinputting the training data into the obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate first obfuscated training data and (ii) a process of inputtingthe first obfuscated training data into the compression network, tothereby allow the compression network to (ii-1) compress the firstobfuscated training data and thus generate first binary training dataand (ii-2) decompress the first binary training data and thus generatefirst compression adaptive obfuscated training data having the datacompression information, wherein, at the step of (b), the learningdevice performs or supports another device to perform (i) a process ofinputting the first compression adaptive obfuscated training data intothe first learning network, to thereby allow the first learning networkto (i-1) apply the learning operation to the first compression adaptiveobfuscated training data by using the first learned parameters of thefirst learning network and thus (i-2) output (1_1)-st characteristicinformation for training on the first compression adaptive obfuscatedtraining data and (ii) a process of inputting the training data into thefirst learning network, to thereby allow the first learning network to(ii-1) apply the learning operation to the training data by using thefirst learned parameters and thus (ii-2) output (2_1)-st characteristicinformation for training on the training data, wherein, at the step of(c), the learning device performs or supports another device to perform(i) a process of training the obfuscation network such that at least one(1_1)-st error, calculated by referring to the (1_1)-st characteristicinformation for training and the (2_1)-st characteristic information fortraining, is minimized and such that at least one (2_1)-st error, whichis a first obfuscated training data score, corresponding to the firstobfuscated training data inputted into the discriminator or the firstcompression adaptive obfuscated training data inputted into thediscriminator, is maximized, to thereby allow the obfuscation network tobe a first trained obfuscation network and (ii) a process of trainingthe discriminator such that a first training data score, correspondingto the modified training data inputted into the discriminator or firstmodified obfuscated training data inputted into the discriminator, ismaximized and such that the first obfuscated training data score isminimized, to thereby allow the discriminator to be a first traineddiscriminator, wherein the first modified obfuscated training data iscreated by modifying the first obfuscated training data and wherein,while increasing an integer k from 2 to n, the learning device performsor supports another device to perform (i) a process of inputting thetraining data into the (k-1)-th trained obfuscation network, to therebyallow the (k-1)-th trained obfuscation network to obfuscate the trainingdata and thus to generate k-th obfuscated training data and a process ofinputting the k-th obfuscated training data into the compressionnetwork, to thereby allow the compression network to (1) compress thek-th obfuscated training data and thus generate k-th binary trainingdata and (2) decompress the k-th binary training data and thus generatek-th compression adaptive obfuscated training data having the datacompression information, (ii) (ii-1) a process of inputting the k-thcompression adaptive obfuscated training data into a k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the k-th compression adaptive obfuscated trainingdata by using one or more k-th learned parameters of the k-th learningnetwork and thus to output (1_k)-th characteristic information fortraining on the k-th compression adaptive obfuscated training data and(ii-2) a process of inputting the training data into the k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the training data by using the k-th learnedparameters and thus to output (2_k)-th characteristic information fortraining on the training data, and (iii) (iii-1) a process of trainingthe (k-1)-th trained obfuscation network such that at least one (1_k)-therror, calculated by referring to the (1_k)-th characteristicinformation for training and the (2_k)-th characteristic information fortraining, is minimized and such that at least one (2_k)-th error, whichis a k-th obfuscated training data score, corresponding to the k-thobfuscated training data inputted into a (k-1)-th trained discriminatoror the k-th compression adaptive obfuscated training data inputted intothe (k-1)-th trained discriminator, is maximized, to thereby allow the(k-1)-th trained obfuscation network to be a k-th trained obfuscationnetwork and (iii-2) a process of training the (k-1)-th traineddiscriminator such that a k-th training data score, corresponding to themodified training data inputted into the (k-1)-th trained discriminatoror k-th modified obfuscated training data inputted into the (k-1)-thtrained discriminator, is maximized and such that the k-th obfuscatedtraining data is minimized, to thereby allow the (k-1)-th traineddiscriminator to be a k-th trained discriminator, wherein the k-thmodified obfuscated training data is created by modifying the k-thobfuscated training data.

As one example, a maximum of the training data score, corresponding tothe modified training data inputted into the discriminator or themodified obfuscated training data inputted into the discriminator, is 1as a value for determining the modified training data or the modifiedobfuscated training data as real and wherein a minimum of the obfuscatedtraining data score, corresponding to the obfuscated training datainputted into the discriminator or the compression adaptive obfuscatedtraining data inputted into the discriminator, is 0 as a value fordetermining the obfuscated training data or the compression adaptiveobfuscated training data as fake.

As one example, at the step of (c), the learning device performs orsupports another device to perform a process of calculating the firsterror by referring to a difference between the first characteristicinformation for training and the second characteristic information fortraining and a process of calculating the second error by referring to(1) a difference between (1-a) the modified training data or themodified obfuscated training data and (1-b) the obfuscated training dataor (2) a difference between (2-a) the modified training data or themodified obfuscated training data and (2-b) the compression adaptiveobfuscated training data.

As one example, the learning device performs or supports another deviceto perform a process of acquiring the first error by referring to a normor a cosine similarity between the first characteristic information fortraining and the second characteristic information for training.

As one example, at the step of (c), the learning device performs orsupports another device to perform a process of calculating the firsterror by further referring to at least one class loss which iscalculated by referring to (1) each of probabilities that each piece ofthe first characteristic information for training, each piece of whichis mapped onto each class, belongs to its corresponding class and (2) aground truth corresponding to the training data.

As one example, at the step of (c), the learning device performs orsupports another device to perform a process of measuring at least onequality by referring to at least part of an entropy of the compressionadaptive obfuscated training data and a degree of noise of thecompression adaptive obfuscated training data and a process of acquiringthe first error by further referring to the measured quality.

In accordance with another aspect of the present disclosure, there isprovided a method for testing an obfuscation network to be used forobfuscating original data to protect personal information, includingsteps of: a testing device, on condition that the learning device hasperformed or supported another device to perform (i) a process ofinputting training data into an obfuscation network, to thereby allowthe obfuscation network to obfuscate the training data and thus togenerate obfuscated training data and a process of inputting theobfuscated training data into a compression network, to thereby allowthe compression network to (1) compress the obfuscated training data andthus generate binary training data and (2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata having the data compression information, (ii) (ii-1) a process ofinputting the compression adaptive obfuscated training data into alearning network having one or more learned parameters, to thereby allowthe learning network to apply a learning operation to the compressionadaptive obfuscated training data by using the learned parameters andthus to output first characteristic information for training on thecompression adaptive obfuscated training data and (ii-2) a process ofinputting the training data into the learning network, to thereby allowthe learning network to apply the learning operation to the trainingdata by using the learned parameters and thus to output secondcharacteristic information for training on the training data, and (iii)a process of training the obfuscation network such that at least onefirst error, calculated by referring to the first characteristicinformation for training and the second characteristic information fortraining, is minimized and such that at least one second error, which iscalculated by referring to (iii-1) (iii-1-a) modified training data,created by modifying the training data, or modified obfuscated trainingdata, created by modifying the obfuscated training data, and (iii-1-b)the obfuscated training data or (iii-2) (iii-2-a) the modified trainingdata or the modified obfuscated training data and (iii-2-b) thecompression adaptive obfuscated training data, is maximized, performingor supporting another device to perform a process of acquiring testdata; and (b) the testing device performing or supporting another deviceto perform a process of inputting the test data into the obfuscationnetwork, to thereby allow the obfuscation network to obfuscate the testdata by using the learned parameters of the obfuscation network and thusto output obfuscated test data having the data compression informationas concealed test data.

As one example, at the step of (a), the learning network includes afirst learning network to an n-th learning network respectively havingone or more first parameters to one or more n-th learned parameterswherein n is an integer greater than 0, and wherein the learning devicehas performed or supported another device to perform (i) a process ofinputting the compression adaptive obfuscated training data into each ofthe first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data, (ii) a process of inputting the training datainto each of the first learning network to the n-th learning network, tothereby allow each of the first learning network to the n-th learningnetwork to (ii-1) apply its corresponding learning operation to thetraining data by using respectively the first learned parameters to then-th learned parameters, and thus (ii-2) output each piece of (2_1)-stcharacteristic information for training to (2_n)-th characteristicinformation for training on the training data, and (iii) a process oftraining the obfuscation network such that the first error is minimizedwhich is an average over (iii-1) the (1_1)-st error acquired byreferring to the (1_1)-st characteristic information for training andthe (2_1)-st characteristic information for training to (iii-2) the(1_n)-th error acquired by referring to the (1_n)-th characteristicinformation for training and the (2_n)-th characteristic information fortraining and such that the second error is maximized which is calculatedby referring to (iii-3) (iii-3-a) the modified training data or themodified obfuscated training data and (iii-3-b) the obfuscated trainingdata or (iii-4) (iii-4-a) the modified training data or the modifiedobfuscated training data and (iii-4-b) the compression adaptiveobfuscated training data.

As one example, at the step of (a), upon acquiring an obfuscatedtraining data score, as the second error, corresponding to theobfuscated training data inputted into a discriminator capable ofdetermining whether its inputted data is real or fake or the compressionadaptive obfuscated training data inputted into the discriminator, thelearning device has performed or supported another device to perform aprocess of training the obfuscation network such that the first error isminimized and the second error is maximized and a process of trainingthe discriminator such that a training data score, corresponding to themodified training data inputted into the discriminator or the modifiedobfuscated training data inputted into the discriminator, is maximizedand such that the obfuscated training data score is minimized.

As one example, at the step of (a), the learning network includes a 1-stlearning network to an n-th learning network respectively having one ormore 1-st learned parameters to one or more n-th learned parameterswherein n is an integer greater than 0, and wherein the learning devicehas performed or supported another device to perform (i) a process ofinputting the compression adaptive obfuscated training data into each ofthe first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data, (ii) a process of inputting the training datainto each of the first learning network to the n-th learning network, tothereby allow each of the first learning network to the n-th learningnetwork to (ii-1) apply its corresponding learning operation to thetraining data by using respectively the first learned parameters to then-th learned parameters, and thus (ii-2) output each piece of (2_1)-stcharacteristic information for training to (2_n)-th characteristicinformation for training on the training data, and (iii) a process oftraining the obfuscation network such that the first error is minimizedwhich is an average over (iii-1) the (1_1)-st error acquired byreferring to the (1_1)-st characteristic information for training andthe (2_1)-st characteristic information for training to (iii-2) the(1_n)-th error acquired by referring to the (1_n)-th characteristicinformation for training and the (2_n)-th characteristic information fortraining and such that the second error which is the obfuscated trainingdata score, corresponding to the obfuscated training data inputted intothe discriminator or the compression adaptive obfuscated training datainputted into the discriminator, is maximized and (iv) a process oftraining the discriminator such that the training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized.

In accordance with still another aspect of the present disclosure, thereis provided a learning device for training an obfuscation network to beused for obfuscating original data to protect personal information,including: at least one memory that stores instructions; and at leastone processor configured to execute the instructions to perform orsupport another device to perform: (I) if training data is acquired, (i)a process of inputting the training data into an obfuscation network, tothereby allow the obfuscation network to obfuscate the training data andthus to generate obfuscated training data, and (ii) a process ofinputting the obfuscated training data into a compression network, tothereby allow the compression network to (ii-1) compress the obfuscatedtraining data and thus generate binary training data and (ii-2)decompress the binary training data and thus generate compressionadaptive obfuscated training data having data compression information,(II) (i) a process of inputting the compression adaptive obfuscatedtraining data into a learning network having one or more learnedparameters, to thereby allow the learning network to (i-1) apply alearning operation to the compression adaptive obfuscated training databy using the learned parameters and thus (i-2) generate firstcharacteristic information for training corresponding to the compressionadaptive obfuscated training data and (ii) a process of inputting thetraining data into the learning network, to thereby allow the learningnetwork to (ii-1) apply the learning operation to the training data byusing the learned parameters and thus (ii-2) generate secondcharacteristic information for training corresponding to the trainingdata, and (III) a process of training the obfuscation network such that(i) at least one first error, calculated by referring to the firstcharacteristic information for training and the second characteristicinformation for training, is minimized and (ii) at least one seconderror, calculated by referring to (ii-1) (ii-1-a) modified trainingdata, created by modifying the training data, or modified obfuscatedtraining data, created by modifying the obfuscated training data, and(ii-1-b) the obfuscated training data or (ii-2) (ii-2-a) the modifiedtraining data or the modified obfuscated training data and (ii-2-b) thecompression adaptive obfuscated training data, is maximized.

As one example, the learning network includes a first learning networkto an n-th learning network respectively having one or more firstlearned parameters to one or more n-th learned parameters wherein n isan integer greater than 0, wherein, at the process of (II), theprocessor performs or supports another device to perform (i) a processof inputting the compression adaptive obfuscated training data into eachof the first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into each of the first learning network to the n-th learningnetwork, to thereby allow each of the first learning network to the n-thlearning network to (ii-1) apply its corresponding learning operation tothe training data by using respectively the first learned parameters tothe n-th learned parameters, and thus (ii-2) output each piece of(2_1)-st characteristic information for training to (2_n)-thcharacteristic information for training on the training data, andwherein, at the process of (III), the processor performs or supportsanother device to perform a process of training the obfuscation networksuch that the first error, which is an average over (1) a (1_1)-st errorcalculated by referring to the (1_1)-st characteristic information fortraining and the (2_1)-st characteristic information for training to (2)a (1_n)-th error calculated by referring to the (1_n)-th characteristicinformation for training and the (2_n)-th characteristic information fortraining, is minimized and such that the second error is maximized.

As one example, the learning network includes a first learning networkto an n-th learning network respectively having one or more firstlearned parameters to one or more n-th learned parameters wherein n isan integer greater than 0, wherein, at the process of (I), the processorperforms or supports another device to perform (i) a process ofinputting the training data into the obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate first obfuscated training data and (ii) a process of inputtingthe first obfuscated training data into the compression network, tothereby allow the compression network to (ii-1) compress the firstobfuscated training data and thus generate first binary training dataand (ii-2) decompress the first binary training data and thus generatefirst compression adaptive obfuscated training data having the datacompression information, wherein, at the process of (II), the processorperforms or supports another device to perform (i) a process ofinputting the first compression adaptive obfuscated training data intothe first learning network, to thereby allow the first learning networkto (i-1) apply the learning operation to the first compression adaptiveobfuscated training data by using the first learned parameters of thefirst learning network and thus (i-2) output (1_1)-st characteristicinformation for training on the first compression adaptive obfuscatedtraining data and (ii) a process of inputting the training data into thefirst learning network, to thereby allow the first learning network to(ii-1) apply the learning operation to the training data by using thefirst learned parameters and thus (ii-2) output (2_1)-st characteristicinformation for training on the training data, wherein, at the processof (III), the processor performs or supports another device to perform aprocess of training the obfuscation network such that (i) at least one(1_1)-st error, calculated by referring to the (1_1)-st characteristicinformation for training and the (2_1)-st characteristic information fortraining, is minimized and (ii) at least one (2_1)-st error, calculatedby referring to (ii-1) (ii-1-a) the modified training data or firstmodified obfuscated training data created by modifying the firstobfuscated training data and (ii-1-b) the first obfuscated training dataor (ii-2) (ii-2-a) the modified training data or the first modifiedobfuscated training data and (ii-2-b) the first compression adaptiveobfuscated training data, is maximized, to thereby allow the obfuscationnetwork to be a first trained obfuscation network, and wherein, whileincreasing an integer k from 2 to n, the processor performs or supportsanother device to perform (i) a process of inputting the training datainto the (k-1)-th trained obfuscation network, to thereby allow the(k-1)-th trained obfuscation network to obfuscate the training data andthus to generate k-th obfuscated training data and a process ofinputting the k-th obfuscated training data into the compressionnetwork, to thereby allow the compression network to (1) compress thek-th obfuscated training data and thus generate k-th binary trainingdata and (2) decompress the k-th binary training data and thus generatek-th compression adaptive obfuscated training data having the datacompression information, (ii) (ii-1) a process of inputting the k-thcompression adaptive obfuscated training data into a k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the k-th compression adaptive obfuscated trainingdata by using one or more k-th learned parameters of the k-th learningnetwork and thus to output (1_k)-th characteristic information fortraining on the k-th compression adaptive obfuscated training data and(ii-2) a process of inputting the training data into the k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the training data by using the k-th learnedparameters and thus to output (2_k)-th characteristic information fortraining on the training data, and (iii) a process of training the(k-1)-th trained obfuscation network such that at least one (1_k)-therror, calculated by referring to the (1_k)-th characteristicinformation for training and the (2_k)-th characteristic information fortraining, is minimized and such that at least one (2_k)-th error, whichis calculated by referring to (iii-1) (iii-1-a) the modified trainingdata or k-th modified obfuscated training data calculated by modifyingthe k-th obfuscated training data and (iii-1-b) the k-th obfuscatedtraining data or (iii-2) (iii-2-a) the modified training data or thek-th modified obfuscated training data and (iii-2-b) the k-thcompression adaptive obfuscated training data, is maximized, to therebyallow the (k-1)-th trained obfuscation network to be a k-th trainedobfuscation network.

As one example, at the process of (III), on condition that an obfuscatedtraining data score, corresponding to the obfuscated training datainputted into a discriminator capable of determining whether itsinputted data is real or fake or the compression adaptive obfuscatedtraining data inputted into the discriminator, has been acquired as thesecond error, the processor performs or supports another device toperform a process of training the obfuscation network such that thefirst error is minimized and the second error is maximized and a processof training the discriminator such that a training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized.

As one example, the learning network includes a first learning networkto an n-th learning network respectively having one or more firstlearned parameters to one or more n-th learned parameters wherein n isan integer greater than 0, wherein, at the process of (II), theprocessor performs or supports another device to perform (i) a processof inputting the compression adaptive obfuscated training data into eachof the first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into each of the first learning network to the n-th learningnetwork, to thereby allow each of the first learning network to the n-thlearning network to (ii-1) apply its corresponding learning operation tothe training data by using respectively the first learned parameters tothe n-th learned parameters, and thus (ii-2) output each piece of(2_1)-st characteristic information for training to (2_n)-thcharacteristic information for training on the training data, andwherein, at the process of (III), the processor performs or supportsanother device to perform (i) a process of training the obfuscationnetwork such that the first error, which is an average over (i-1) atleast one (1_1)-st error calculated by referring to the (1_1)-stcharacteristic information for training and the (2_1)-st characteristicinformation for training to (i-2) at least one (1_n)-th error calculatedby referring to the (1_n)-th characteristic information for training andthe (2_n)-th characteristic information for training, is minimized andsuch that the second error, which is the obfuscated training data score,corresponding to the obfuscated training data inputted into thediscriminator or the compression adaptive obfuscated training datainputted into the discriminator, is maximized and (ii) a process oftraining the discriminator such that the training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized.

As one example, the learning network includes a first learning networkto an n-th learning network respectively having one or more firstlearned parameters to one or more n-th learned parameters wherein n isan integer greater than 0, wherein, at the process of (I), the processorperforms or supports another device to perform (i) a process ofinputting the training data into the obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate first obfuscated training data and (ii) a process of inputtingthe first obfuscated training data into the compression network, tothereby allow the compression network to (ii-1) compress the firstobfuscated training data and thus generate first binary training dataand (ii-2) decompress the first binary training data and thus generatefirst compression adaptive obfuscated training data having the datacompression information, wherein, at the process of (II), the processorperforms or supports another device to perform (i) a process ofinputting the first compression adaptive obfuscated training data intothe first learning network, to thereby allow the first learning networkto (i-1) apply the learning operation to the first compression adaptiveobfuscated training data by using the first learned parameters of thefirst learning network and thus (i-2) output (1_1)-st characteristicinformation for training on the first compression adaptive obfuscatedtraining data and (ii) a process of inputting the training data into thefirst learning network, to thereby allow the first learning network to(ii-1) apply the learning operation to the training data by using thefirst learned parameters and thus (ii-2) output (2_1)-st characteristicinformation for training on the training data, wherein, at the processof (III), the processor performs or supports another device to perform(i) a process of training the obfuscation network such that at least one(1_1)-st error, calculated by referring to the (1_1)-st characteristicinformation for training and the (2_1)-st characteristic information fortraining, is minimized and such that at least one (2_1)-st error, whichis a first obfuscated training data score, corresponding to the firstobfuscated training data inputted into the discriminator or the firstcompression adaptive obfuscated training data inputted into thediscriminator, is maximized, to thereby allow the obfuscation network tobe a first trained obfuscation network and (ii) a process of trainingthe discriminator such that a first training data score, correspondingto the modified training data inputted into the discriminator or firstmodified obfuscated training data inputted into the discriminator, ismaximized and such that the first obfuscated training data score isminimized, to thereby allow the discriminator to be a first traineddiscriminator, wherein the first modified obfuscated training data iscreated by modifying the first obfuscated training data and wherein,while increasing an integer k from 2 to n, the processor performs orsupports another device to perform (i) a process of inputting thetraining data into the (k-1)-th trained obfuscation network, to therebyallow the (k-1)-th trained obfuscation network to obfuscate the trainingdata and thus to generate k-th obfuscated training data and a process ofinputting the k-th obfuscated training data into the compressionnetwork, to thereby allow the compression network to (1) compress thek-th obfuscated training data and thus generate k-th binary trainingdata and (2) decompress the k-th binary training data and thus generatek-th compression adaptive obfuscated training data having the datacompression information, (ii) (ii-1) a process of inputting the k-thcompression adaptive obfuscated training data into a k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the k-th compression adaptive obfuscated trainingdata by using one or more k-th learned parameters of the k-th learningnetwork and thus to output (1_k)-th characteristic information fortraining on the k-th compression adaptive obfuscated training data and(ii-2) a process of inputting the training data into the k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the training data by using the k-th learnedparameters and thus to output (2_k)-th characteristic information fortraining on the training data, and (iii) (iii-1) a process of trainingthe (k-1)-th trained obfuscation network such that at least one (1_k)-therror, calculated by referring to the (1_k)-th characteristicinformation for training and the (2_k)-th characteristic information fortraining, is minimized and such that at least one (2_k)-th error, whichis a k-th obfuscated training data score, corresponding to the k-thobfuscated training data inputted into a (k-1)-th trained discriminatoror the k-th compression adaptive obfuscated training data inputted intothe (k-1)-th trained discriminator, is maximized, to thereby allow the(k-1)-th trained obfuscation network to be a k-th trained obfuscationnetwork and (iii-2) a process of training the (k-1)-th traineddiscriminator such that a k-th training data score, corresponding to themodified training data inputted into the (k-1)-th trained discriminatoror k-th modified obfuscated training data inputted into the (k-1)-thtrained discriminator, is maximized and such that the k-th obfuscatedtraining data is minimized, to thereby allow the (k-1)-th traineddiscriminator to be a k-th trained discriminator, wherein the k-thmodified obfuscated training data is created by modifying the k-thobfuscated training data.

As one example, a maximum of the training data score, corresponding tothe modified training data inputted into the discriminator or themodified obfuscated training data inputted into the discriminator, is 1as a value for determining the modified training data or the modifiedobfuscated training data as real and wherein a minimum of the obfuscatedtraining data score, corresponding to the obfuscated training datainputted into the discriminator or the compression adaptive obfuscatedtraining data inputted into the discriminator, is 0 as a value fordetermining the obfuscated training data or the compression adaptiveobfuscated training data as fake.

As one example, at the process of (III), the processor performs orsupports another device to perform a process of calculating the firsterror by referring to a difference between the first characteristicinformation for training and the second characteristic information fortraining and a process of calculating the second error by referring to(1) a difference between (1-a) the modified training data or themodified obfuscated training data and (1-b) the obfuscated training dataor (2) a difference between (2-a) the modified training data or themodified obfuscated training data and (2-b) the compression adaptiveobfuscated training data.

As one example, the processor performs or supports another device toperform a process of acquiring the first error by referring to a norm ora cosine similarity between the first characteristic information fortraining and the second characteristic information for training.

As one example, at the process of (III), the processor performs orsupports another device to perform a process of calculating the firsterror by further referring to at least one class loss which iscalculated by referring to (1) each of probabilities that each piece ofthe first characteristic information for training, each piece of whichis mapped onto each class, belongs to its corresponding class and (2) aground truth corresponding to the training data.

As one example, at the process of (III), the processor performs orsupports another device to perform a process of measuring at least onequality by referring to at least part of an entropy of the compressionadaptive obfuscated training data and a degree of noise of thecompression adaptive obfuscated training data and a process of acquiringthe first error by further referring to the measured quality.

In accordance with still yet another aspect of the present disclosure,there is provided a testing device for testing an obfuscation network tobe used for obfuscating original data to protect personal information,including: at least one memory that stores instructions; and at leastone processor configured to execute the instructions to perform orsupport another device to perform: (I) on condition that the learningdevice has performed or supported another device to perform (i) aprocess of inputting training data into an obfuscation network, tothereby allow the obfuscation network to obfuscate the training data andthus to generate obfuscated training data and a process of inputting theobfuscated training data into a compression network, to thereby allowthe compression network to (1) compress the obfuscated training data andthus generate binary training data and (2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata (having the data compression information), (ii) (ii-1) a process ofinputting the compression adaptive obfuscated training data into alearning network having one or more learned parameters, to thereby allowthe learning network to apply a learning operation to the compressionadaptive obfuscated training data by using the learned parameters andthus to output first characteristic information for training on thecompression adaptive obfuscated training data and (ii-2) a process ofinputting the training data into the learning network, to thereby allowthe learning network to apply the learning operation to the trainingdata by using the learned parameters and thus to output secondcharacteristic information for training on the training data, and (iii)a process of training the obfuscation network such that at least onefirst error, calculated by referring to the first characteristicinformation for training and the second characteristic information fortraining, is minimized and such that at least one second error, which iscalculated by referring to (iii-1) (iii-1-a) modified training data,created by modifying the training data, or modified obfuscated trainingdata, created by modifying the obfuscated training data, and (iii-1-b)the obfuscated training data or (iii-2) (iii-2-a) the modified trainingdata or the modified obfuscated training data and (iii-2-b) thecompression adaptive obfuscated training data, is maximized, a processof acquiring test data, and (II) a process of inputting the test datainto the obfuscation network, to thereby allow the obfuscation networkto obfuscate the test data by using the learned parameters of theobfuscation network and thus to output obfuscated test data having thedata compression information as concealed test data.

As one example, at the process of (I), the learning network includes afirst learning network to an n-th learning network respectively havingone or more first parameters to one or more n-th learned parameterswherein n is an integer greater than 0, and wherein the learning devicehas performed or supported another device to perform (i) a process ofinputting the compression adaptive obfuscated training data into each ofthe first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data, (ii) a process of inputting the training datainto each of the first learning network to the n-th learning network, tothereby allow each of the first learning network to the n-th learningnetwork to (ii-1) apply its corresponding learning operation to thetraining data by using respectively the first learned parameters to then-th learned parameters, and thus (ii-2) output each piece of (2_1)-stcharacteristic information for training to (2_n)-th characteristicinformation for training on the training data, and (iii) a process oftraining the obfuscation network such that the first error is minimizedwhich is an average over (iii-1) the (1_1)-st error acquired byreferring to the (1_1)-st characteristic information for training andthe (2_1)-st characteristic information for training to (iii-2) the(1_n)-th error acquired by referring to the (1_n)-th characteristicinformation for training and the (2_n)-th characteristic information fortraining and such that the second error is maximized which is calculatedby referring to (iii-3) (iii-3-a) the modified training data or themodified obfuscated training data and (iii-3-b) the obfuscated trainingdata or (iii-4) (iii-4-a) the modified training data or the modifiedobfuscated training data and (iii-4-b) the compression adaptiveobfuscated training data.

As one example, at the process of (I), upon acquiring an obfuscatedtraining data score, as the second error, corresponding to theobfuscated training data inputted into a discriminator capable ofdetermining whether its inputted data is real or fake or the compressionadaptive obfuscated training data inputted into the discriminator, thelearning device has performed or supported another device to perform aprocess of training the obfuscation network such that the first error isminimized and the second error is maximized and a process of trainingthe discriminator such that a training data score, corresponding to themodified training data inputted into the discriminator or the modifiedobfuscated training data inputted into the discriminator, is maximizedand such that the obfuscated training data score is minimized.

As one example, at the process of (I), the learning network includes a1-st learning network to an n-th learning network respectively havingone or more 1-st learned parameters to one or more n-th learnedparameters wherein n is an integer greater than 0, and wherein thelearning device has performed or supported another device to perform (i)a process of inputting the compression adaptive obfuscated training datainto each of the first learning network to the n-th learning network, tothereby allow each of the first learning network to the n-th learningnetwork to (i-1) apply its corresponding learning operation to thecompression adaptive obfuscated training data by using respectively thefirst learned parameters to the n-th learned parameters of the firstlearning network to the n-th learning network, and thus (i-2) outputeach piece of (1_1)-st characteristic information for training to(1_n)-th characteristic information for training on the compressionadaptive obfuscated training data, (ii) a process of inputting thetraining data into each of the first learning network to the n-thlearning network, to thereby allow each of the first learning network tothe n-th learning network to (ii-1) apply its corresponding learningoperation to the training data by using respectively the first learnedparameters to the n-th learned parameters, and thus (ii-2) output eachpiece of (2_1)-st characteristic information for training to (2_n)-thcharacteristic information for training on the training data, and (iii)a process of training the obfuscation network such that the first erroris minimized which is an average over (iii-1) the (1_1)-st erroracquired by referring to the (1_1)-st characteristic information fortraining and the (2_1)-st characteristic information for training to(iii-2) the (1_n)-th error acquired by referring to the (1_n)-thcharacteristic information for training and the (2_n)-th characteristicinformation for training and such that the second error which is theobfuscated training data score, corresponding to the obfuscated trainingdata inputted into the discriminator or the compression adaptiveobfuscated training data inputted into the discriminator, is maximizedand (iv) a process of training the discriminator such that the trainingdata score, corresponding to the modified training data inputted intothe discriminator or the modified obfuscated training data inputted intothe discriminator, is maximized and such that the obfuscated trainingdata score is minimized.

In addition, recordable media that are readable by a computer forstoring a computer program to execute the method of the presentdisclosure are further provided.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings to be used for explaining example embodiments ofthe present disclosure are only part of example embodiments of thepresent disclosure and other drawings can be obtained based on thedrawings by those skilled in the art of the present disclosure withoutinventive work.

FIG. 1 is a drawing schematically illustrating a learning device fortraining an obfuscation network capable of obfuscating, e.g.,anonymizing or concealing, original data in accordance with one exampleembodiment of the present disclosure.

FIG. 2 is a drawing schematically illustrating a method for training theobfuscation network capable of obfuscating, e.g., anonymizing orconcealing, the original data in accordance with one example embodimentof the present disclosure.

FIG. 3 is a drawing schematically illustrating another method fortraining the obfuscation network capable of obfuscating, e.g.,anonymizing or concealing, the original data in accordance with oneexample embodiment of the present disclosure.

FIG. 4 is a drawing schematically illustrating a method for training theobfuscation network capable of obfuscating, e.g., anonymizing orconcealing, the original data in accordance with another exampleembodiment of the present disclosure.

FIG. 5 is a drawing schematically illustrating another method fortraining the obfuscation network capable of obfuscating, e.g.,anonymizing or concealing, the original data in accordance with anotherexample embodiment of the present disclosure.

FIG. 6 is a drawing schematically illustrating a testing device fortesting a trained obfuscation network in accordance with one exampleembodiment of the present disclosure.

FIG. 7 is a drawing schematically illustrating a method for testing thetrained obfuscation network in accordance with one example embodiment ofthe present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following detailed description, reference is made to theaccompanying drawings that show, by way of illustration, specificembodiments in which the present disclosure may be practiced. Theseembodiments are described in sufficient detail to enable those skilledin the art to practice the present disclosure. It is to be understoodthat the various embodiments of the present disclosure, althoughdifferent, are not necessarily mutually exclusive. For example, aparticular feature, structure, or characteristic described herein may beimplemented as being changed from an embodiment to other embodimentswithout departing from the spirit and scope of the present disclosure.In addition, it is to be understood that the position or arrangement ofindividual elements within each embodiment may be modified withoutdeparting from the spirit and scope of the present disclosure. Thefollowing detailed description is, therefore, not to be taken in alimiting sense, and the scope of the present disclosure is described asincluding the appended claims, along with the full range of equivalentsto which the claims are entitled. In the drawings, like numerals referto the same or similar components throughout the several aspects.

To allow those skilled in the art to carry out the present disclosureeasily, the example embodiments of the present disclosure will beexplained in detail as shown below by referring to attached drawings.

FIG. 1 is a drawing schematically illustrating a learning device fortraining an obfuscation network capable of obfuscating, e.g.,anonymizing or concealing, original data in accordance with one exampleembodiment of the present disclosure.

By referring to FIG. 1, the learning device 100 in accordance with oneexample embodiment of the present disclosure may include a memory 110for storing instructions to train the obfuscation network capable ofobfuscating, e.g., anonymizing, training data such that a learningnetwork outputs a result, generated by inputting obfuscated trainingdata into the learning network, same as or similar to a result,generated by inputting the training data into the learning network, anda processor 120 for performing processes to train the obfuscationnetwork according to the instructions in the memory 110.

Specifically, the learning device 100 may typically achieve a desiredsystem performance by using combinations of at least one computingdevice and at least one computer software, e.g., a computer processor, amemory, a storage, an input device, an output device, or any otherconventional computing components, an electronic communication devicesuch as a router or a switch, an electronic information storage systemsuch as a network-attached storage (NAS) device and a storage areanetwork (SAN) as the computing device and any instructions that allowthe computing device to function in a specific way as the computersoftware.

Also, the processors of such devices may include hardware configurationof MPU (Micro Processing Unit) or CPU (Central Processing Unit), cachememory, data bus, etc. Additionally, the computing device may furtherinclude operating system (OS) and software configuration of applicationsthat achieve specific purposes.

Such description of the computing device does not exclude an integrateddevice including any combination of a processor, a memory, a medium, orany other computing components for implementing the present disclosure.

Meanwhile, if the training data is acquired, according to theinstructions stored in the memory 110, the processor 120 of the learningdevice 100 may input the training data into the obfuscation network, tothereby allow the obfuscation network to obfuscate the training data andthus to generate the obfuscated training data. And, the learning device100 may perform or support another device to perform a process ofinputting the obfuscated training data into a compression network, tothereby allow the compression network to (i) compress the obfuscatedtraining data and thus generate binary training data and (ii) decompressthe binary training data and thus generate compression adaptiveobfuscated training data. Next, the learning device 100 may perform orsupport another device to perform (i) a process of inputting thecompression adaptive obfuscated training data into a learning networkhaving one or more learned parameters, to thereby allow the learningnetwork to (i-1) apply a learning operation to the compression adaptiveobfuscated training data by using the learned parameters and thus (i-2)generate first characteristic information for training corresponding tothe compression adaptive obfuscated training data and (ii) a process ofinputting the training data into the learning network, to thereby allowthe learning network to (ii-1) apply the learning operation to thetraining data by using the learned parameters and thus (ii-2) generatesecond characteristic information for training corresponding to thetraining data. Thereafter, the learning device 100 may perform orsupport another device to perform a process of training the obfuscationnetwork such that at least one first error, which is calculated byreferring to at least part of (i) at least one (1_1)-st error acquiredby referring to the first characteristic information for training andthe second characteristic information for training and (ii) at least one(1_2)-nd error acquired by referring to (ii-1) at least one taskspecific output generated by using the first characteristic informationfor training and (ii-2) at least one ground truth corresponding to thetask specific output, is minimized and such that at least one seconderror, which is calculated by referring to (i) (i-1) modified trainingdata, created by modifying the training data, or modified obfuscatedtraining data, created by modifying the obfuscated training data, and(i-2) the obfuscated training data or (ii) (ii-1) the modified trainingdata or the modified obfuscated training data and (ii-2) the compressionadaptive obfuscated training data, is maximized.

Also, on condition that an obfuscated training data score, correspondingto the obfuscated training data inputted into a discriminator capable ofdetermining whether its inputted data is real or fake or the compressionadaptive obfuscated training data inputted into the discriminator, hasbeen acquired as the second error, the learning device 100 may performor support another device to perform a process of training theobfuscation network such that the first error is minimized and thesecond error is maximized and a process of training the discriminatorsuch that a training data score, corresponding to the modified trainingdata inputted into the discriminator or the modified obfuscated trainingdata inputted into the discriminator, is maximized and such that theobfuscated training data score is minimized. Herein, the discriminatormay generate the obfuscated training data score representing whether itsinputted data, i.e., the obfuscated training data or the compressionadaptive obfuscated training data, is real or fake. Further, thediscriminator may also generate the training data score representingwhether its inputted data, i.e., the modified training data or themodified obfuscated training data, is real or fake.

A method for training the obfuscation network capable of obfuscating,e.g., anonymizing or concealing, the original data to protect personalinformation by using the learning device 100 in accordance with oneexample embodiment of the present disclosure is described by referringto FIGS. 2 to 5 as follows.

FIG. 2 is a drawing schematically illustrating a method for training theobfuscation network capable of obfuscating, e.g., anonymizing orconcealing, the original data in accordance with one example embodimentof the present disclosure.

First, if the training data x is acquired, the learning device 100 mayinput the training data x into the obfuscation network O, to therebyallow the obfuscation network O to obfuscate the training data x andthus to generate the obfuscated training data x′, i.e., O(x).

Herein, the obfuscated training data x′ may be recognized as datadifferent from the training data x by a human, but may be recognized asdata similar to or same as the training data x by the learning network.

Meanwhile, as one example, the obfuscation network O may include anencoder having one or more convolutional layers for applying one or moreconvolution operations to images inputted as the training data x, and adecoder having one or more deconvolutional layers for applying one ormore deconvolution operations to at least one feature map outputted fromthe encoder and for generating the obfuscated training data x′, but thescope of the present disclosure is not limited thereto, and may includeany learning networks having various structures capable of obfuscatingthe inputted training data.

Next, the learning device 100 may perform or support another device toperform a process of inputting the obfuscated training data x′ into thecompression network C, to thereby allow the compression network C to (i)compress the obfuscated training data x′ and thus generate the binarytraining data and (ii) decompress the binary training data and thusgenerate compression adaptive obfuscated training data x″ having datacompression information. Herein, the data compression information asdescribed below may be a data compression ratio, but the scope of thepresent disclosure is not limited thereto.

Herein, the compression network C may be used for allowing theobfuscation network O to obfuscate, e.g., to anonymize or conceal, theoriginal data such that, for example, the data compression ratio of theobfuscated original data is similar to that of the original data duringthe processes of training the obfuscation network O.

As one example, if the original data is an image, since the obfuscateddata may be regarded as an image with a similar format, it is commonthat a data compression ratio of the obfuscated data similar to that ofthe original data is required. Therefore, the obfuscated training datax′ may be inputted into the compression network C, to thereby allow thecompression network C to (i) compress the obfuscated training data x′ byusing data compression algorithm and thus generate the binary trainingdata and (ii) decompress the binary training data and thus generate thecompression adaptive obfuscated training data x″ having the datacompression information.

And, algorithms for compressing and decompressing data may include JPEG,MPEG, etc., but the scope of the present disclosure is not limitedthereto, and may include any data compression and decompressionalgorithms used for compressing and decompressing data.

Next, the learning device 100 may perform or support another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data x″ into the learning network F having the learnedparameters, to thereby allow the learning network F to (i-1) apply thelearning operation to the compression adaptive obfuscated training datax″ by using the learned parameters and thus (i-2) generate firstcharacteristic information F(x″) corresponding to the compressionadaptive obfuscated training data x″ and (ii) a process of inputting thetraining data x into the learning network F, to thereby allow thelearning network F to (ii-1) apply the learning operation to thetraining data x by using the learned parameters and thus (ii-2) generatesecond characteristic information F(x) corresponding to the trainingdata x.

Herein, the learning network F may include a machine learning network,but the scope of the present disclosure is not limited thereto, and mayinclude any learning networks capable of using their respective learnedparameters, generating the first characteristic information F(x″) byapplying the learning operation to the compression adaptive obfuscatedtraining data x″, and generating the second characteristic informationF(x) by applying the learning operation to the training data x. And, themachine learning network may include at least one of a k-NearestNeighbors, a Linear Regression, a Logistic Regression, a Support VectorMachine (SVM), a Decision Tree and Random Forest, a Neural Network, aClustering, a Visualization and a Dimensionality Reduction, anAssociation Rule Learning, a Deep Belief Network, a ReinforcementLearning, and a Deep learning algorithm, but the machine learningnetwork is not limited thereto and may include various learningalgorithms. Also, a subject to be obfuscated, e.g., a subject to beanonymized or concealed, may be personal information included in theoriginal data, i.e., the training data x. Herein, the personalinformation may include any information related to a person, such aspersonal identification information, personal medical information,personal biometric information, personal behavioral information, etc.

And, the first characteristic information F(x″) and the secondcharacteristic information F(x) may be features or logits respectivelycorresponding to the compression adaptive obfuscated training data x″and the training data x. Also, the first characteristic informationF(x″) and the second characteristic information F(x) may be featurevalues related to certain features respectively in the compressionadaptive obfuscated training data x″ and the training data x, or thelogits including values related to at least one of vectors, matrices,and coordinates related to the certain features. For example, if thetraining data x are facial image data, the result above may be classesfor face recognition, facial features, e.g., laughing expressions,coordinates of facial landmark points, e.g., both end points on farsides of an eye.

Next, the learning device 100 may perform or support another device toperform a process of training the obfuscation network O such that thefirst error, which is calculated by referring to at least part of (i)the (1_1)-st error acquired by referring to the first characteristicinformation F(x″) and the second characteristic information F(x) and(ii) the (1_2)-nd error acquired by referring to (ii-1) the taskspecific output generated by using the first characteristic informationand (ii-2) the ground truth corresponding to the task specific output,is minimized and such that the second error, which is calculated byreferring to (i) (i-1) the modified training data, created by modifyingthe training data, or the modified obfuscated training data, created bymodifying the obfuscated training data, and (i-2) the obfuscatedtraining data x′ or (ii) (ii-1) the modified training data or themodified obfuscated training data and (ii-2) the compression adaptiveobfuscated training data x″, is maximized. That is, the learning device100 may train the obfuscation network O, (i) by using the second error,such that the obfuscation network O outputs the obfuscated training datax′ much different from the training data x and (ii) by using the firsterror, such that the obfuscation network O obfuscates the training datax to output the obfuscated training data x′, in order to allow thelearning network F to recognize the obfuscated training data x′ as sameas or similar to the training data x. Also, the obfuscation network Omay be trained to output the obfuscated training data x′ having a datacompression ratio similar to that of the training data x.

Herein, the learning device 100 may acquire the first error by referringto at least part of (1) a difference between the first characteristicinformation F(x″) and the second characteristic information F(x) and (2)a difference between the task specific output and its correspondingground truth. As one example, the learning device 100 may acquire thefirst error by referring to a norm or a cosine similarity between thefirst characteristic information F(x″) and the second characteristicinformation F(x), but the scope of the present disclosure is not limitedthereto, and any various algorithms capable of calculating a differencebetween the first characteristic information F(x″) and the secondcharacteristic information F(x) may be used. Also, the learning device100 may perform or support another device to perform a process ofcalculating the second error by referring to (1) a difference between(1-a) the modified training data or the modified obfuscated trainingdata and (1-b) the obfuscated training data x′ or (2) a differencebetween (2-a) the modified training data or the modified obfuscatedtraining data and (2-b) the compression adaptive obfuscated trainingdata x″.

Herein, the modified training data or the modified obfuscated trainingdata may be respectively generated by adding at least one random noise,created through the random noise generating network (not illustrated),to the training data or the obfuscated training data. As one example,the random noise generating network may be instructed to generate therandom noise having the normal distribution N(0, σ), and the generatednoise may be added to the training data or the obfuscated training data,to thereby generate the modified training data or the modifiedobfuscated training data. As another example, the modified training dataor the modified obfuscated training data may be respectively generatedby blurring the training data or the obfuscated training data, orchanging a resolution of the training data or the obfuscated trainingdata, as well as adding the random noise, but the scope of the presentdisclosure is not limited thereto, and various ways of modifying thetraining data or the obfuscated training data may be used.

Also, the learning device 100 may measure at least one quality byreferring to at least part of an entropy of the compression adaptiveobfuscated training data x″ and a degree of noise of the compressionadaptive obfuscated training data x″, and may acquire the first error byfurther referring to the measured quality. That is, the learning device100 may train the obfuscation network O such that the quality of thecompression adaptive obfuscated training data x″ is minimized, forexample, such that the entropy, noise, etc. of the compression adaptiveobfuscated training data x″ are maximized.

And, if the learning device 100 trains the obfuscation network O suchthat the first error is minimized and that the second error ismaximized, the learning device 100 may fix and not update the learnedparameters of the learning network F, and may proceed with training theobfuscation network O only.

Meanwhile, the task specific output may be an output of a task to beperformed by the learning network F, and may have various resultsaccording to the task learned by the learning network F, such as aprobability of a class for classification, coordinates resulting fromregression for location detection, etc., and an activation function ofan activation unit may be applied to characteristic informationoutputted from the learning network F, to thereby generate the taskspecific output according to the task to be performed by the learningnetwork F. Herein, the activation function may include a sigmoidfunction, a linear function, a softmax function, an rlinear function, asquare function, a sqrt function, an srlinear function, an abs function,a tanh function, a brlinear function, etc. but the scope of the presentdisclosure is not limited thereto.

As one example, when the learning network F performs the task for theclassification, the learning device 100 may map the first characteristicinformation outputted from the learning network F onto each of classes,to thereby generate one or more probabilities of the compressionadaptive obfuscated training data, for each of the classes. Herein, theprobabilities for each of the classes may represent probabilities of thefirst characteristic information F(x″), outputted for each of theclasses from the learning network F, being correct. For example, if thetraining data are the facial image data, a probability of the facehaving a laughing expression may be outputted as 0.75, and a probabilityof the face not having the laughing expression may be outputted as 0.25,and the like. Herein, a softmax algorithm may be used for mapping thefirst characteristic information F(x″) outputted from the learningnetwork F onto each of the classes, but the scope of the presentdisclosure is not limited thereto, and various algorithms may be usedfor mapping the first characteristic information F(x″) onto each of theclasses.

And during the processes of training the obfuscation network O with thefirst error by the learning device 100, the compression network C maytransmit a gradient to the obfuscation network O under the assumptionthat the data compression algorithm is C(x′)=x′ in a backward pass,unlike in a forward pass where the actual data compression algorithm isused. That is, the binary data generated in the forward pass may be sameas a result of compressing O(x), i.e., x′, with the data compressionalgorithm, and as a result, the compression network C may serve as anidentity function in the backward pass.

FIG. 3 is a drawing schematically illustrating another method fortraining the obfuscation network capable of obfuscating, e.g.,anonymizing or concealing, the original data in accordance with oneexample embodiment of the present disclosure. Herein, the learningnetwork F in FIG. 2 is configured as multiple learning networks F1, F2,. . . , and Fn having one or more respective learned parameters.Further, each of the multiple learning networks F1, F2, . . . , and Fnmay have completed learning to perform tasks at least part of which maybe different from one another. In the description below, the part easilydeducible from the explanation of FIG. 2 will be omitted.

First, if the training data x is acquired, the learning device 100 mayinput the training data x into the obfuscation network O, to therebyallow the obfuscation network O to obfuscate the training data x andthus to generate the obfuscated training data x′, i.e., O(x).

Herein, the obfuscated training data x′ may be recognized as datadifferent from the training data x by a human, but may be recognized asdata similar to or same as the training data x by the learning network.

Next, the learning device 100 may perform or support another device toperform a process of inputting the obfuscated training data x′ into thecompression network C, to thereby allow the compression network C to (i)compress the obfuscated training data x′ and thus generate the binarytraining data and (ii) decompress the binary training data and thusgenerate compression adaptive obfuscated training data x″ having datacompression information.

Herein, the compression network C may be used for allowing theobfuscation network O to obfuscate, e.g., to conceal or anonymize, theoriginal data such that, for example, a data compression ratio of theobfuscated original data is similar to that of the original data duringthe processes of training the obfuscation network O.

As one example, if the original data is an image, since the obfuscateddata may be regarded as an image with a similar format, it is commonthat a data compression ratio of the obfuscated data similar to that ofthe original data is required. Therefore, the obfuscated training datax′ may be inputted into the compression network C, to thereby allow thecompression network C to (i) compress the obfuscated training data x′ byusing data compression algorithm and thus generate the binary trainingdata and (ii) decompress the binary training data and thus generate thecompression adaptive obfuscated training data x″ having the datacompression information.

Next, the learning device 100 may input the compression adaptiveobfuscated training data x″ into each of the first learning network F1to the n-th learning network Fn, to thereby allow each of the firstlearning network F1 to the n-th learning network Fn to (i) apply itscorresponding learning operation to the compression adaptive obfuscatedtraining data x″ by using respectively the first learned parameters tothe n-th learned parameters of the first learning network F1 to the n-thlearning network Fn, and thus (ii) generate each piece of (1_1)-stcharacteristic information F1(n″) to (1_n)-th characteristic informationFn(x″) corresponding to the compression adaptive obfuscated trainingdata x″. Also, the learning device 100 may input the training data xinto each of the first learning network F1 to the n-th learning networkFn, to thereby allow each of the first learning network F1 to the n-thlearning network Fn to (i) apply its corresponding learning operation tothe training data x by using respectively the first learned parametersto the n-th learned parameters of the first learning network F1 to then-th learning network Fn, and thus (ii) generate each piece of (2_1)-stcharacteristic information F1(x) to (2_n)-th characteristic informationFn(x) corresponding to the training data x.

Next, the learning device 100 may train the obfuscation network O suchthat the first error is minimized which is calculated by referring to atleast part of (i) the (1_1)-st error which is an average over (i-1) a(1_1)_1-st error, acquired by referring to the (1_1)-st characteristicinformation F1(x″) and the (2_1)-st characteristic information F1(x), to(i-2) a (1_1)_n-th error acquired by referring to the (1_n)-thcharacteristic information Fn(x″) and the (2_n)-th characteristicinformation Fn(x) and (ii) the (1_2)-nd error which is an average over(ii-1) a (1_2)_1-st error acquired by referring to (ii-1-a) at least onefirst task specific output created by using the (1_1)-st characteristicinformation F1(x″) and (ii-1-b) at least one first ground truthcorresponding to the first task specific output to (ii-2) a (1_2)_n-therror acquired by referring to (ii-2-a) at least one n-th task specificoutput created by using the (1_n)-th characteristic information Fn(x″)and (ii-2-b) at least one n-th ground truth corresponding to the n-thtask specific output and such that the second error is maximized whichis calculated by referring to (i) (i-1) the modified training data orthe modified obfuscated training data and (i-2) the obfuscated trainingdata x′ or (ii) (ii-1) the modified training data or the modifiedobfuscated training data and (ii-2) the compression adaptive obfuscatedtraining data x″.

That is, the learning device 100 may (i) acquire the (1_1)_1-st errorcalculated by referring to the (1_1)-st characteristic informationF1(x″) and the (2_1)-st characteristic information F1(x), (ii) acquirethe (1_1)_2-nd error calculated by referring to the (1_2)-ndcharacteristic information F2(x″) and the (2_2)-nd characteristicinformation F2(x), and similarly, (iii) acquire the (1_1)_n-th errorcalculated by referring to the (1_n)-th characteristic informationFn(x″) and the (2_n)-th characteristic information Fn(x), and thus (iv)acquire the (1_1)-st error which is an average over the acquired(1_1)_1-st error to the acquired (1_1)_n-th error. Then, the learningdevice 100 may acquire (i) the (1_2)_1-st error calculated by referringto (i-1) the first task specific output created by using the (1_1)-stcharacteristic information F1(x″) and (i-2) the first ground truthcorresponding to the first task specific output to (ii) the (1_2)_n-therror calculated by referring to (ii-1) the n-th task specific outputcreated by using the (1_n)-th characteristic information Fn(x″) and(ii-2) the n-th ground truth corresponding to the n-th task specificoutput, and thus, acquire the (1_2)-nd error which is an average overthe acquired (1_2)_1-st error to the (1_2)_n-th error. And, the learningdevice 100 may train the obfuscation network O such that the firsterror, which is calculated by referring to at least part of the (1_1)-sterror and the (1_2)-nd error, is minimized and such that the seconderror is maximized. That is, the first error may be one of the (1_1)-sterror, the (1_2)-nd error, and a sum of the (1_1)-st error and the(1_2)-nd error, but the scope of the present disclosure is not limitedthereto.

Also, the learning device 100 may measure at least one quality byreferring to at least part of an entropy of the compression adaptiveobfuscated training data x″ and a degree of noise of the compressionadaptive obfuscated training data x″, and may acquire the first error byfurther referring to the measured quality. That is, the learning device100 may train the obfuscation network O such that the quality of thecompression adaptive obfuscated training data x″ is minimized, forexample, such that the entropy, noise, etc. of the compression adaptiveobfuscated training data x″ are maximized.

And, if the learning device 100 trains the obfuscation network O suchthat the first error is minimized and that the second error ismaximized, the learning device 100 may fix and not update the learnedparameters of the learning network F, and may proceed with training theobfuscation network O only.

Meanwhile, in the above description, the learning device 100 may trainthe obfuscation network O such that the first error is minimized whichis calculated by referring to at least part of (i) the (1_1)-st errorwhich is an average over (i-1) the (1_1)_1-st error acquired byreferring to the (1_1)-st characteristic information F1(x″) and the(2_1)-st characteristic information F1(x) to (i-2) the (1_1)_n-th erroracquired by referring to the (1_n)-th characteristic information Fn(x″)and the (2_n)-th characteristic information Fn(x) and (ii) the (1_2)-nderror which is an average over (ii-1) the (1_2)_1-st error acquired byreferring to (ii-1-a) the first task specific output created by usingthe (1_1)-st characteristic information F1(x″) and (ii-1-b) the firstground truth corresponding to the first task specific output to (ii-2)the (1_2)_n-th error acquired by referring to (ii-2-a) the n-th taskspecific output created by using the (1_n)-th characteristic informationand (ii-2-b) the n-th ground truth corresponding to the n-th taskspecific output and such that the second error is maximized which iscalculated by referring to (i) (i-1) the modified training data or themodified obfuscated training data and (i-2) the obfuscated training dataor (ii) (ii-2) the modified training data or the modified obfuscatedtraining data and (ii-2) the compression adaptive obfuscated trainingdata. However, as another example, the obfuscation network O may besequentially trained such that (i) the (1_1)-st error, calculated byreferring to at least part of the (1_1)_1-st error and the (1_2)_1-sterror, to (ii) the (1_n)-th error, calculated by referring to at leastpart of the (1_1)_n-th error and the (1_2)_n-th error, are minimized.

That is, the learning device 100 may perform or support another deviceto perform a process of inputting the training data x into theobfuscation network O, to thereby allow the obfuscation network O toobfuscate the training data x and thus to generate first obfuscatedtraining data x1′ and a process of inputting the first obfuscatedtraining data x1′ into the compression network C, to thereby allow thecompression network C to (i) compress the first obfuscated training datax1′ and thus generate first binary training data and (ii) decompress thefirst binary training data and thus generate first compression adaptiveobfuscated training data x1″. And, the learning device 100 may performor support another device to perform (i) a process of inputting thefirst compression adaptive obfuscated training data x1″ into the firstlearning network F1, to thereby allow the first learning network F1 to(i-1) apply the learning operation to the first compression adaptiveobfuscated training data x1″ by using the first learned parameters ofthe first learning network F1 and thus (i-2) output the (1_1)-stcharacteristic information F1(x 1″) corresponding to the firstcompression adaptive obfuscated training data x1″, and (ii) a process ofinputting the training data x into the first learning network F1, tothereby allow the first learning network F1 to (ii-1) apply the learningoperation to the training data x by using the first learned parametersand thus (ii-2) output the (2_1)-st characteristic information F1(x)corresponding to the training data x. Thereafter, the learning device100 may perform or support another device to perform a process oftraining the obfuscation network O such that the (1_1)-st error isminimized which is calculated by referring to at least part of (i) the(1_1)_1-st error acquired by referring to the (1_1)-st characteristicinformation F1(x 1″) and the (2_1)-st characteristic information F1(x)and (ii) the (1_2)_1-st error acquired by referring to (ii-1) the firsttask specific output generated by using the (1_1)-st characteristicinformation F1(x″) and (ii-2) the first ground truth corresponding tothe first task specific output and such that the (2_1)-st error ismaximized which is calculated by referring to (i) (i-1) the modifiedtraining data or the first modified obfuscated training data and (i-2)the first obfuscated training data x1′ or (ii) (ii-1) the modifiedtraining data or the first modified obfuscated training data and (ii-2)the first compression adaptive obfuscated training data x1″, to therebyallow the obfuscation network O to be a first trained obfuscationnetwork O1. Herein, the first modified obfuscated training data may becreated by modifying the first obfuscated training data.

And, while increasing an integer k from 2 to n, the learning device 100may repeat the processes above up to the n-th learning network Fn, tothereby acquire an n-th obfuscation network On.

That is, the learning device 100 may perform or support another deviceto perform (i) a process of inputting the training data x into a(k-1)-th trained obfuscation network O(k-1), to thereby allow the(k-1)-th trained obfuscation network O(k-1) to obfuscate the trainingdata x and thus to generate k-th obfuscated training data xk′ and (ii) aprocess of inputting the k-th obfuscated training data xk′ into thecompression network C, to thereby allow the compression network C to(ii-1) compress the k-th obfuscated training data xk′ and thus generatek-th binary training data and (ii-2) decompress the k-th binary trainingdata and thus generate k-th compression adaptive obfuscated trainingdata xk″. And, the learning device 100 may perform or support anotherdevice to perform (i) a process of inputting the k-th compressionadaptive obfuscated training data xk″ into the k-th learning network Fk,to thereby allow the k-th learning network Fk to apply the learningoperation to the k-th compression adaptive obfuscated training data xk″by using the k-th learned parameters of the k-th learning network Fk andthus to output (1_k)-th characteristic information Fk(xk″) correspondingto the k-th compression adaptive obfuscated training data xk″, and (ii)a process of inputting the training data x into the k-th learningnetwork Fk, to thereby allow the k-th learning network Fk to apply thelearning operation to the training data x by using the k-th learnedparameters and thus to output (2_k)-th characteristic information Fk(xk)corresponding to the training data x. Thereafter, the learning device100 may perform or support another device to perform a process oftraining the (k-1)-th trained obfuscation network O(k-1) such that the(1_k)-th error is minimized which is calculated by referring to at leastpart of (i) the (1_1)_k-th error acquired by referring to the (1_k)-thcharacteristic information Fk(xk″) and the (2_k)-th characteristicinformation Fk(x) and (ii) the (1_2)_k-th error acquired by referring to(ii-1) the k-th task specific output generated by using the (1_k)-thcharacteristic information and (ii-2) the k-th ground truthcorresponding to the k-th task specific output and such that the(2_k)-th error is maximized which is calculated by referring to (i)(i-1) the modified training data or the k-th modified obfuscatedtraining data and (i-2) the k-th obfuscated training data xk′ or (ii)(ii-1) the modified training data or the k-th modified obfuscatedtraining data and (ii-2) the k-th compression adaptive obfuscatedtraining data xk″, to thereby allow the (k-1)-th trained obfuscationnetwork O(k-1) to be a k-th trained obfuscation network Ok.

FIG. 4 is a drawing schematically illustrating a method for training theobfuscation network capable of obfuscating, e.g., anonymizing orconcealing, the original data in accordance with another exampleembodiment of the present disclosure. In the description below, the parteasily deducible from the explanation of FIGS. 2 and 3 will be omitted.

First, if the training data x is acquired, the learning device 100 mayinput the training data x into the obfuscation network O, to therebyallow the obfuscation network O to obfuscate the training data x andthus to generate the obfuscated training data x′, i.e., O(x).

Next, the learning device 100 may perform or support another device toperform a process of inputting the obfuscated training data x′ into thecompression network C, to thereby allow the compression network C to (i)compress the obfuscated training data x′ and thus generate binarytraining data and (ii) decompress the binary training data and thusgenerate compression adaptive obfuscated training data x″.

Next, the learning device 100 may perform or support another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data x″ into the learning network F having one or more learnedparameters, to thereby allow the learning network F to (i-1) apply alearning operation to the compression adaptive obfuscated training datax″ by using the learned parameters and thus (i-2) generate firstcharacteristic information F(x″) corresponding to the compressionadaptive obfuscated training data x″ and (ii) a process of inputting thetraining data x into the learning network F, to thereby allow thelearning network F to (ii-1) apply the learning operation to thetraining data x by using the learned parameters and thus (ii-2) generatesecond characteristic information F(x) corresponding to the trainingdata x.

Next, on condition that an obfuscated training data score, correspondingto the obfuscated training data inputted into a discriminator D capableof determining whether its inputted data is real or fake or thecompression adaptive obfuscated training data x″ inputted into thediscriminator D, has been acquired as the second error, the learningdevice 100 may perform or support another device to perform (i) aprocess of training the obfuscation network O such that the first erroris minimized and the second error, i.e., the obfuscated training datascore, is maximized and (ii) a process of training the discriminator Dsuch that a training data score, corresponding to the modified trainingdata inputted into the discriminator D or the modified obfuscatedtraining data inputted into the discriminator D, is maximized and suchthat the obfuscated training data score is minimized.

That is, the learning device 100 may train the obfuscation network O,(i) by using the first error, such that the obfuscation network Oobfuscates the training data x to output the obfuscated training data x′in order to allow the obfuscated training data x′ to be recognized bythe learning network F as same as or similar to the training data x and(ii) by using the second error, such that the obfuscation network Oobfuscates the training data x to output the obfuscated training data x′in order to allow the obfuscated training data x′, different from thetraining data x, to be difficult to differentiate from the training datax, the obfuscated training data x′ having data compression information.

Herein, a maximum of the training data score, corresponding to themodified training data inputted into the discriminator D or the modifiedobfuscated training data inputted into the discriminator D, may be 1 asa value for determining the modified training data or the modifiedobfuscated training data as real. And a minimum of the obfuscatedtraining data score, corresponding to the obfuscated training datainputted into the discriminator D or the compression adaptive obfuscatedtraining data x″ inputted into the discriminator D, may be 0 as a valuefor determining the obfuscated training data or the compression adaptiveobfuscated training data x″ as fake. That is, the discriminator D may betrained to recognize the obfuscated training data or the compressionadaptive obfuscated training data x″ as the modified training data orthe modified obfuscated training data.

FIG. 5 is a drawing schematically illustrating another method fortraining the obfuscation network capable of obfuscating, e.g.,anonymizing or concealing, the original data in accordance with anotherexample embodiment of the present disclosure. Herein, the learningnetwork F in FIG. 4 is configured as multiple learning networks F1, F2,. . . , and Fn having one or more respective learned parameters. In thedescription below, the part easily deducible from the explanation ofFIGS. 2 to 4 will be omitted.

First, if the training data x is acquired, the learning device 100 mayinput the training data x into the obfuscation network O, to therebyallow the obfuscation network O to obfuscate the training data x andthus to generate the obfuscated training data x′, i.e., O(x).

Next, the learning device 100 may perform or support another device toperform a process of inputting the obfuscated training data x′ into thecompression network C, to thereby allow the compression network C to (i)compress the obfuscated training data x′ and thus generate binarytraining data and (ii) decompress the binary training data and thusgenerate compression adaptive obfuscated training data x″.

Next, the learning device 100 may input the compression adaptiveobfuscated training data x″ into each of the first learning network F1to the n-th learning network Fn, to thereby allow each of the firstlearning network F1 to the n-th learning network Fn to (i) apply itscorresponding learning operation to the compression adaptive obfuscatedtraining data x″ by using respectively the first learned parameters tothe n-th learned parameters of the first learning network F1 to the n-thlearning network Fn, and thus (ii) generate each piece of (1_1)-stcharacteristic information F1(f) to (1_n)-th characteristic informationFn(x″) corresponding to the compression adaptive obfuscated trainingdata x″. Also, the learning device 100 may input the training data xinto each of the first learning network F1 to the n-th learning networkFn, to thereby allow each of the first learning network F1 to the n-thlearning network Fn to (i) apply its corresponding learning operation tothe training data x by using respectively the first learned parametersto the n-th learned parameters of the first learning network F1 to then-th learning network Fn, and thus (ii) generate each piece of (2_1)-stcharacteristic information F1(x) to (2_n)-th characteristic informationFn(x) corresponding to the training data x.

Next, the learning device 100 may perform or support another device toperform a process of training the obfuscation network O such that thefirst error is minimized which is calculated by referring to at leastpart of (i) a (1_1)-st error which is an average over (i-1) a (1_1)_1-sterror acquired by referring to the (1_1)-st characteristic informationF1(x″) and the (2_1)-st characteristic information F1(x) to (i-2) a(1_1)_n-th error acquired by referring to the (1_n)-th characteristicinformation Fn(x″) and the (2_n)-th characteristic information Fn(x) and(ii) a (1_2)-nd error which is an average over (ii-1) a (1_2)_1-st erroracquired by referring to (ii-1-a) a first task specific output createdby using the (1_1)-st characteristic information F1(x″) and (ii-1-b) thefirst ground truth corresponding to the first task specific output to(ii-2) a (1_2)_n-th error acquired by referring to (ii-2-a) an n-th taskspecific output created by using the (1_n)-th characteristic informationFn(x″) and (ii-2-b) the n-th ground truth corresponding to the n-th taskspecific output and such that the second error is maximized which is theobfuscated training data score corresponding to the obfuscated trainingdata inputted into the discriminator D or the compression adaptiveobfuscated training data x″ inputted into the discriminator D. And thelearning device 100 may perform or support another device to perform aprocess of training the discriminator D such that the training datascore, corresponding to the modified training data inputted into thediscriminator D or the modified obfuscated training data inputted intothe discriminator D, is maximized and such that the obfuscated trainingdata score is minimized. Herein, the discriminator D may generate thetraining data score representing whether its inputted data, i.e., themodified training data or the modified obfuscated training data, is realor fake.

That is, the learning device 100 may (i) acquire the (1_1)_1-st errorcalculated by referring to the (1_1)-st characteristic informationF1(x″) and the (2_1)-st characteristic information F1(x), (ii) acquirethe (1_1)_2-nd error calculated by referring to the (1_2)-ndcharacteristic information F2(x″) and the (2_2)-nd characteristicinformation F2(x), and similarly, (iii) acquire the (1_1)_n-th errorcalculated by referring to the (1_n)-th characteristic informationFn(x″) and the (2_n)-th characteristic information Fn(x), and thus (iv)acquire the (1_1)-st error which is an average over the acquired(1_1)_1-st error to the acquired (1_1)_n-th error. Then, the learningdevice 100 may acquire (i) the (1_2)_1-st error calculated by referringto (i-1) the first task specific output created by using the (1_1)-stcharacteristic information F1(x″) and (i-2) the first ground truthcorresponding to the first task specific output to (ii) the (1_2)_n-therror calculated by referring to (ii-1) the n-th task specific outputcreated by using the (1_n)-th characteristic information Fn(x″) and(ii-2) the n-th ground truth corresponding to the n-th task specificoutput, and thus acquire the (1_2)-nd error which is an average over theacquired (1_2)_1-st error to the (1_2)_n-th error. And, the learningdevice 100 may train the obfuscation network O such that the firsterror, which is calculated by referring to at least part of the (1_1)-sterror and the (1_2)-nd error, is minimized and such that the seconderror is maximized.

Meanwhile, in the above description, the learning device 100 may performor support another device to perform a process of training theobfuscation network O such that the first error is minimized which iscalculated by referring to at least part of (i) the (1_1)-st error whichis an average over (i-1) the (1_1)_1-st error acquired by referring tothe (1_1)-st characteristic information F1(x″) and the (2_1)-stcharacteristic information F1(x) to (i-2) the (1_1)_n-th error acquiredby referring to the (1_n)-th characteristic information Fn(x″) and the(2_n)-th characteristic information Fn(x), and (ii) the (1_2)-nd errorwhich is an average over (ii-1) the (1_2)_1-st error acquired byreferring to (ii-1-a) the first task specific output created by usingthe (1_1)-st characteristic information F1(x″) and (ii-1-b) the firstground truth corresponding to the first task specific output to (ii-2)the (1_2)_n-th error acquired by referring to (ii-2-a) the n-th taskspecific output created by using the (1_n)-th characteristic informationand (ii-2-b) the n-th ground truth corresponding to the n-th taskspecific output and such that the second error is maximized which is theobfuscated training data score corresponding to the obfuscated trainingdata inputted into the discriminator D or the compression adaptiveobfuscated training data inputted into the discriminator D. As anotherexample, the obfuscation network O may be sequentially trained such that(i) the (1_1)-st error calculated by referring to at least part of the(1_1)_1-st error and the (1_2)_1-st error to (ii) the (1_n)-th errorcalculated by referring to at least part of the (1_1)_n-th error and the(1_2)_n-th error are minimized.

That is, the learning device 100 may perform or support another deviceto perform (i) a process of inputting the training data x into theobfuscation network O, to thereby allow the obfuscation network O toobfuscate the training data x and thus to generate first obfuscatedtraining data x1′ and (ii) a process of inputting the first obfuscatedtraining data x1′ into the compression network C, to thereby allow thecompression network C to (ii-1) compress the first obfuscated trainingdata x1′ and thus generate first binary training data and (ii-2)decompress the first binary training data and thus generate firstcompression adaptive obfuscated training data x1″. And, the learningdevice 100 may perform or support another device to perform (i) aprocess of inputting the first compression adaptive obfuscated trainingdata x1″ into the first learning network F1, to thereby allow the firstlearning network F1 to (i-1) apply the learning operation to the firstcompression adaptive obfuscated training data x1″ by using the firstlearned parameters of the first learning network F1 and thus (i-2)output the (1_1)-st characteristic information F1(x 1″) corresponding tothe first compression adaptive obfuscated training data x1″, and (ii) aprocess of inputting the training data x into the first learning networkF1, to thereby allow the first learning network F1 to (ii-1) apply thelearning operation to the training data x by using the first learnedparameters and thus (ii-2) output the (2_1)-st characteristicinformation F1(x) corresponding to the training data x. Thereafter, thelearning device 100 may perform or support another device to perform aprocess of training the obfuscation network O such that the (1_1)-sterror is minimized which is calculated by referring to at least part of(i) the (1_1)_1-st error acquired by referring to the (1_1)-stcharacteristic information F1(x 1″) and the (2_1)-st characteristicinformation F1(x), and (ii) the (1_2)_1-st error acquired by referringto (ii-1) the first task specific output generated by using the (1_1)-stcharacteristic information F1(f) and (ii-2) the first ground truthcorresponding to the first task specific output and such that the(2_1)-st error is maximized which is the first obfuscated training datascore, corresponding to the first obfuscated training data inputted intothe discriminator D or the first compression adaptive obfuscatedtraining data x1″ inputted into the discriminator D, to thereby allowthe obfuscation network O to be a first trained obfuscation network O1.Herein, the discriminator D may generate the first obfuscated trainingdata score representing whether its inputted data, i.e., the firstobfuscated training data or the first compression adaptive obfuscatedtraining data, is real or fake. And the learning device 100 may performor support another device to perform a process of training thediscriminator D such that the first training data score, correspondingto the modified training data inputted into the discriminator D or thefirst modified obfuscated training data inputted into the discriminatorD, is maximized and such that the first obfuscated training data scoreis minimized, to thereby allow the discriminator D to be a first traineddiscriminator Dl. Herein, the discriminator D may generate the firsttraining data score representing whether its inputted data, i.e., themodified training data or the first modified obfuscated training data,is real or fake.

And, while increasing an integer k from 2 to n, the learning device 100may repeat the processes above up to the n-th learning network Fn, tothereby acquire an n-th obfuscation network On.

That is, the learning device 100 may perform or support another deviceto perform (i) a process of inputting the training data x into the(k-1)-th trained obfuscation network O(k-1), to thereby allow the(k-1)-th trained obfuscation network O(k-1) to obfuscate the trainingdata x and thus to generate k-th obfuscated training data xk′ and (ii) aprocess of inputting the k-th obfuscated training data xk′ into thecompression network C, to thereby allow the compression network C to(ii-1) compress the k-th obfuscated training data xk′ and thus generatek-th binary training data and (ii-2) decompress the k-th binary trainingdata and thus generate k-th compression adaptive obfuscated trainingdata xk″. And, the learning device 100 may perform or support anotherdevice to perform (i) a process of inputting the k-th compressionadaptive obfuscated training data xk″ into the k-th learning network Fk,to thereby allow the k-th learning network Fk to apply a learningoperation to the k-th compression adaptive obfuscated training data xk″by using one or more k-th learned parameters of the k-th learningnetwork Fk and thus to output the (1_k)-th characteristic informationFk(xk″) corresponding to the k-th compression adaptive obfuscatedtraining data xk″ and (ii) a process of inputting the training data xinto the k-th learning network Fk, to thereby allow the k-th learningnetwork Fk to apply the learning operation to the training data x byusing the k-th learned parameters and thus to output the (2_k)-thcharacteristic information Fk(xk) corresponding to the training data x.Thereafter, the learning device 100 may perform or support anotherdevice to perform a process of training the (k-1)-th trained obfuscationnetwork O(k-1) such that a (1_k)-th error is minimized which iscalculated by referring to at least part of (i) a (1_1)_k-th erroracquired by referring to the (1_k)-th characteristic information Fk(xk″)and the (2_k)-th characteristic information Fk(x), and (ii) a (1_2)_k-therror acquired by referring to (ii-1) a k-th task specific outputgenerated by using the (1_k)-th characteristic information Fk(x″) and(ii-2) a k-th ground truth corresponding to the k-th task specificoutput and such that a (2_k)-th error is maximized which is a k-thobfuscated training data score, corresponding to k-th obfuscatedtraining data inputted into a (k-1)-th trained discriminator D(k-1) ork-th compression adaptive obfuscated training data xk″ inputted into the(k-1)-th trained discriminator D(k-1), to thereby allow the (k-1)-thtrained obfuscation network O(k-1) to be a k-th trained obfuscationnetwork O(k). Herein, the (k-1)-th trained discriminator D(k-1) maygenerate the k-th obfuscated training data score representing whetherits inputted data, i.e., the k-th obfuscated training data or the k-thcompression adaptive obfuscated training data, is real or fake. And thelearning device 100 may perform or support another device to perform aprocess of training the (k-1)-th trained discriminator D(k-1) such thata k-th training data score, corresponding to the modified training datainputted into the (k-1)-th trained discriminator D(k-1) or the k-thmodified obfuscated training data inputted into the (k-1)-th traineddiscriminator D(k-1), is maximized and such that the k-th obfuscatedtraining data score is minimized, to thereby allow the (k-1)-th traineddiscriminator D(k-1) to be a k-th trained discriminator D(k). Herein,the (k-1)-th trained discriminator D(k-1) may generate the k-th trainingdata score representing whether its inputted data, i.e., the modifiedtraining data or the k-th modified obfuscated training data, is real orfake.

FIG. 6 is a drawing schematically illustrating a testing device fortesting a trained obfuscation network in accordance with one exampleembodiment of the present disclosure.

By referring to FIG. 6, the testing device 200 in accordance with oneexample embodiment of the present disclosure may include a memory 210for storing instructions to test the trained obfuscation network whichhas been trained to obfuscate test data such that the learning networkoutputs a result, generated by inputting obfuscated test data includingits corresponding data compression information into the learningnetwork, same as or similar to a result, generated by inputting the testdata into the learning network, and a processor 220 for performingprocesses to test the trained obfuscation network according to theinstructions in the memory 210.

Specifically, the testing device 200 may typically achieve a desiredsystem performance by using combinations of at least one computingdevice and at least one computer software, e.g., a computer processor, amemory, a storage, an input device, an output device, or any otherconventional computing components, an electronic communication devicesuch as a router or a switch, an electronic information storage systemsuch as a network-attached storage (NAS) device and a storage areanetwork (SAN) as the computing device and any instructions that allowthe computing device to function in a specific way as the computersoftware.

Also, the processors of such devices may include hardware configurationof MPU (Micro Processing Unit) or CPU (Central Processing Unit), cachememory, data bus, etc. Additionally, the computing device may furtherinclude operating system (OS) and software configuration of applicationsthat achieve specific purposes.

Such description of the computing device does not exclude an integrateddevice including any combination of a processor, a memory, a medium, orany other computing components for implementing the present disclosure.

Meanwhile, before processes of testing the trained obfuscation networkare performed, the processes of training the obfuscation network asdescribed above may be performed. That is, the learning device may haveperformed or supported another device to perform (i) a process ofinputting training data into the obfuscation network, to thereby allowthe obfuscation network to obfuscate the training data and thus togenerate obfuscated training data and a process of inputting theobfuscated training data into the compression network, to thereby allowthe compression network to (i-1) compress the obfuscated training dataand thus generate binary training data and (i-2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata having its corresponding data compression information, (ii) (ii-1)a process of inputting the compression adaptive obfuscated training datainto the learning network having the learned parameters, to therebyallow the learning network to apply a learning operation to thecompression adaptive obfuscated training data by using the learnedparameters and thus to output first characteristic information fortraining on the compression adaptive obfuscated training data and (ii-2)a process of inputting the training data into the learning network, tothereby allow the learning network to apply the learning operation tothe training data by using the learned parameters and thus to outputsecond characteristic information for training on the training data, and(iii) a process of training the obfuscation network such that the firsterror, calculated by referring to at least part of (iii-1) the (1_1)-sterror acquired by referring to the first characteristic information fortraining and the second characteristic information for training, and(iii-2) the (1_2)-nd error acquired by referring to (iii-2-a) the taskspecific output created by using the first characteristic informationfor training and (iii-2-b) the ground truth corresponding to the taskspecific output, is minimized and such that the second error, calculatedby referring to (iii-3) (iii-3-a) the modified training data or themodified obfuscated training data and (iii-3-b) the obfuscated trainingdata or (iii-4) (iii-4-a) the modified training data or the modifiedobfuscated training data and (iii-4-b) the compression adaptiveobfuscated training data, is maximized. After such processes of trainingthe obfuscated network have been performed, if test data is acquired,the processor 220 of the testing device 200 may perform or supportanother device to perform, according to the instructions stored in thememory 210, a process of inputting the test data into the obfuscationnetwork, to thereby allow the obfuscation network to obfuscate the testdata by using the learned parameters of the obfuscation network and thusto output obfuscated test data, having its corresponding datacompression information, as anonymized test data or concealed test data.

Also, before the processes of testing the trained obfuscation networkare performed, another example of the processes of training theobfuscation network as described above may be performed. That is, thelearning device may have performed or supported another device toperform (i) a process of inputting the training data into theobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate obfuscated trainingdata and a process of inputting the obfuscated training data into thecompression network, to thereby allow the compression network to (i-1)compress the obfuscated training data and thus generate binary trainingdata and (i-2) decompress the binary training data and thus generatecompression adaptive obfuscated training data having its correspondingdata compression information, (ii) (ii-1) a process of inputting thecompression adaptive obfuscated training data into the learning networkhaving the learned parameters, to thereby allow the learning network toapply the learning operation to the compression adaptive obfuscatedtraining data by using the learned parameters and thus to output firstcharacteristic information for training on the compression adaptiveobfuscated training data and (ii-2) a process of inputting the trainingdata into the learning network, to thereby allow the learning network toapply the learning operation to the training data by using the learnedparameters and thus to output second characteristic information fortraining on the training data, (iii) a process of training theobfuscation network such that the first error, calculated by referringto at least part of (iii-1) the (1_1)-st error acquired by referring tothe first characteristic information for training and the secondcharacteristic information for training, and (iii-2) the (1_2)-nd erroracquired by referring to (iii-2-a) the task specific output created byusing the first characteristic information for training and (iii-2-b)the ground truth corresponding to the task specific output, is minimizedand such that the second error which is the obfuscated training datascore, corresponding to the obfuscated training data inputted into thediscriminator capable of determining whether its inputted data is realor fake or the compression adaptive obfuscated training data inputtedinto the discriminator, is maximized, and (iv) a process of training thediscriminator such that the training data score, corresponding to themodified training data inputted into the discriminator or the modifiedobfuscated training data inputted into the discriminator, is maximizedand such that the obfuscated training data score is minimized. Aftersuch processes of training the obfuscated network have been performed,if the test data is acquired, the processor 220 of the testing device200 may perform or support another device to perform, according to theinstructions stored in the memory 210, a process of inputting the testdata into the obfuscation network, to thereby allow the obfuscationnetwork to obfuscate the test data by using the learned parameters ofthe obfuscation network and thus to output the obfuscated test data,having its corresponding data compression information, as the anonymizedtest data or the concealed test data.

FIG. 7 is a drawing schematically illustrating a method for testing thetrained obfuscation network in accordance with one example embodiment ofthe present disclosure.

By referring to FIG. 7, the testing device 200 may input the test data,for example, original images on a left side of FIG. 7, into theobfuscation network O which has been trained to obfuscate the originaldata such that the learning network outputs a result, generated byinputting obfuscated data including its corresponding data compressioninformation into the learning network, same as or similar to a result,generated by inputting the original data into the learning network, andallow the obfuscation network O to obfuscate the test data according tothe learned parameters and thus to output the obfuscated test data,e.g., obfuscated images on a right side of FIG. 7, including the datacompression information.

For reference, the left side of FIG. 7 is a drawing exemplarilyillustrating 64 image samples selected from the CIFAR-10 dataset whichincludes images collected and labeled by Canadian Institute for AdvancedResearch (CIFAR) for image classification.

The obfuscated data generated by obfuscating, e.g., anonymizing orconcealing, the image samples on the left side of FIG. 7 used as theoriginal data, in accordance with the present disclosure, are shown onthe right side of FIG. 7.

By referring to FIG. 7, 64 pieces of the obfuscated data on the rightside of FIG. 7 which are obfuscated, e.g., anonymized or concealed, inaccordance with the present disclosure are visually different from 64pieces of the original data on the left side of FIG. 7, but if the 64pieces of the obfuscated data are inputted into the learning network,the learning network outputs a result same as or similar to that of theoriginal data.

Meanwhile, the trained obfuscation network O may have been trainedbeforehand by processes similar to those in description of FIGS. 2 to 5.

That is, the learning device may have performed or supported anotherdevice to perform (i) a process of inputting training data into theobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate the obfuscated trainingdata and a process of inputting the obfuscated training data into thecompression network, to thereby allow the compression network to (i-1)compress the obfuscated training data and thus generate binary trainingdata and (i-2) decompress the binary training data and thus generatecompression adaptive obfuscated training data having the datacompression information, (ii) (ii-1) a process of inputting thecompression adaptive obfuscated training data into the learning networkhaving the learned parameters, to thereby allow the learning network toapply the learning operation to the compression adaptive obfuscatedtraining data by using the learned parameters and thus to output thefirst characteristic information for training on the compressionadaptive obfuscated training data and (ii-2) a process of inputting thetraining data into the learning network, to thereby allow the learningnetwork to apply the learning operation to the training data by usingthe learned parameters and thus to output the second characteristicinformation for training on the training data, and (iii) a process oftraining the obfuscation network such that the first error, calculatedby referring to at least part of (iii-1) the (1_1)-st error acquired byreferring to the first characteristic information for training and thesecond characteristic information for training, and (iii-2) the (1_2)-nderror acquired by referring to (iii-2-a) the task specific outputcreated by using the first characteristic information for training and(iii-2-b) the ground truth corresponding to the task specific output, isminimized and such that the second error, calculated by referring to(iii-3) (iii-3-a) the modified training data or the modified obfuscatedtraining data and (iii-3-b) the obfuscated training data or (iii-4)(iii-4-a) the modified training data or the modified obfuscated trainingdata and (iii-4-b) the compression adaptive obfuscated training data, ismaximized, to thereby allow the obfuscation network to be the trainedobfuscation network O.

Also, in the above description, the learning network may include thefirst learning network to the n-th learning network respectively havingthe first learned parameters to the n-th learned parameters, and thelearning device may have performed or supported another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data into each of the first learning network to the n-thlearning network, to thereby allow each of the first learning network tothe n-th learning network to (i-1) apply its corresponding learningoperation to the compression adaptive obfuscated training data by usingrespectively the first learned parameters to the n-th learned parametersof the first learning network to the n-th learning network, and thus(i-2) output each piece of the (1_1)-st characteristic information fortraining to the (1_n)-th characteristic information for training on thecompression adaptive obfuscated training data, (ii) a process ofinputting the training data into each of the first learning network tothe n-th learning network, to thereby allow each of the first learningnetwork to the n-th learning network to (ii-1) apply its correspondinglearning operation to the training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (ii-2) output each pieceof the (2_1)-st characteristic information for training to the (2_n)-thcharacteristic information for training on the training data, and (iii)a process of training the obfuscation network such that the first erroris minimized which is calculated by referring to at least part of(iii-1) the (1_1)-st error which is an average over (iii-1-a) the(1_1)_1-st error acquired by referring to the (1_1)-st characteristicinformation for training and the (2_1)-st characteristic information fortraining to (iii-1-b) the (1_1)_n-th error acquired by referring to the(1_n)-th characteristic information for training and the (2_n)-thcharacteristic information for training, and (iii-2) the (1_2)-nd errorwhich is an average over (iii-2-a) the (1_2)_1-st error acquired byreferring to the first task specific output, created by using the(1_1)-st characteristic information for training, and to the firstground truth corresponding to the first task specific output to(iii-2-b) the (1_2)_n-th error acquired by referring to the n-th taskspecific output, created by using the (1_n)-th characteristicinformation for training, and to the n-th ground truth corresponding tothe n-th task specific output, and such that the second error ismaximized which is calculated by referring to (iii-3) (iii-3-a) themodified training data or the modified obfuscated training data and(iii-3-b) the obfuscated training data or (iii-4) (iii-4-a) the modifiedtraining data or the modified obfuscated training data and (iii-4-b) thecompression adaptive obfuscated training data.

And, the learning device may have performed or supported another deviceto perform (i) a process of inputting the training data into theobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate obfuscated trainingdata and a process of inputting the obfuscated training data into thecompression network, to thereby allow the compression network to (i-1)compress the obfuscated training data and thus generate binary trainingdata and (i-2) decompress the binary training data and thus generatecompression adaptive obfuscated training data having the datacompression information, (ii) (ii-1) a process of inputting thecompression adaptive obfuscated training data into the learning networkhaving the learned parameters, to thereby allow the learning network toapply the learning operation to the compression adaptive obfuscatedtraining data by using the learned parameters and thus to output thefirst characteristic information for training on the compressionadaptive obfuscated training data and (ii-2) a process of inputting thetraining data into the learning network, to thereby allow the learningnetwork to apply the learning operation to the training data by usingthe learned parameters and thus to output the second characteristicinformation for training on the training data, (iii) a process oftraining the obfuscation network such that the first error is minimizedwhich is calculated by referring to at least part of (iii-1) the(1_1)-st error which is an average over (iii-1-a) the (1_1)_1-st errorcalculated by referring to the (1_1)-st characteristic information fortraining and the (2_1)-st characteristic information for training to(iii-1-b) the (1_1)_n-th error calculated by referring to the (1_n)-thcharacteristic information for training and the (2_n)-th characteristicinformation for training, and (iii-2) the (1_2)-nd error which is anaverage over (iii-2-a) the (1_2)_1-st error calculated by referring tothe first task specific output, created by using the (1_1)-stcharacteristic information for training, and to the first ground truthcorresponding to the first task specific output to (iii-2-b) the(1_2)_n-th error calculated by referring to the n-th task specificoutput, created by using the (1_n)-th characteristic information fortraining, and to the n-th ground truth corresponding to the n-th taskspecific output and such that the second error which is the obfuscatedtraining data score, corresponding to the obfuscated training datainputted into the discriminator capable of determining whether itsinputted data is real or fake or the compression adaptive obfuscatedtraining data inputted into the discriminator, is maximized, and (iv) aprocess of training the discriminator such that the training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized.

Also, in the above description, the learning network may include thefirst learning network to the n-th learning network respectively havingthe first learned parameters to the n-th learned parameters, and thelearning device may have performed or supported another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data into each of the first learning network to the n-thlearning network, to thereby allow each of the first learning network tothe n-th learning network to (i-1) apply its corresponding learningoperation to the compression adaptive obfuscated training data by usingrespectively the first learned parameters to the n-th learned parametersof the first learning network to the n-th learning network, and thus(i-2) output each piece of the (1_1)-st characteristic information fortraining to the (1_n)-th characteristic information for training on thecompression adaptive obfuscated training data, (ii) a process ofinputting the training data into each of the first learning network tothe n-th learning network, to thereby allow each of the first learningnetwork to the n-th learning network to (ii-1) apply its correspondinglearning operation to the training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (ii-2) output each pieceof the (2_1)-st characteristic information for training to the (2_n)-thcharacteristic information for training on the training data, (iii) aprocess of training the obfuscation network such that the first error isminimized which is calculated by referring to at least part of (iii-1)the (1_1)-st error which is an average over (iii-1-a) the (1_1)_1-sterror acquired by referring to the (1_1)-st characteristic informationfor training and the (2_1)-st characteristic information for training to(iii-1-b) the (1_1)_n-th error acquired by referring to the (1_n)-thcharacteristic information for training and the (2_n)-th characteristicinformation for training, and (iii-2) the (1_2)-nd error which is anaverage over (iii-2-a) the (1_2)_1-st error acquired by referring to thefirst task specific output, created by using the (1_1)-st characteristicinformation for training, and to the first ground truth corresponding tothe first task specific output to (iii-2-b) the (1_2)_n-th erroracquired by referring to the n-th task specific output, created by usingthe (1_n)-th characteristic information for training, and to the n-thground truth corresponding to the n-th task specific output, and suchthat the second error is maximized which is the obfuscated training datascore corresponding to the modified training data inputted into thediscriminator or the compression adaptive obfuscated training datainputted into the discriminator and (iv) a process of training thediscriminator such that the training data score, corresponding to themodified training data inputted into the discriminator or the modifiedobfuscated training data inputted into the discriminator, is maximizedand such that the obfuscated training data score is minimized.

Meanwhile, the obfuscated data, including the data compressioninformation, which are obfuscated, e.g., anonymized or concealed, by thetrained obfuscation network in accordance with the present disclosuremay be provided or sold to a buyer of big data of images.

Also, in accordance with one example embodiment of the presentdisclosure, when the obfuscated image data, e.g., anonymized image dataor concealed image data, are provided or sold to the buyer, the testingmethod of the trained obfuscation network may be provided as implementedin a form of program instructions executable by a variety of computercomponents and recorded to computer readable media. In accordance withone example embodiment of the present disclosure, the buyer may executethe program instructions recorded in the computer readable media byusing the computer devices, to thereby generate obfuscated data, e.g.,anonymized data or concealed data, from the original data owned by thebuyer or acquired from other sources, and use the obfuscated data forhis/her own learning network. Also, the buyer may use at least two ofthe obfuscated data, the original image data owned by the buyer oracquired from other sources, and the obfuscated image data provided orsold to the buyer, together for the buyer's learning network.

Meanwhile, if the testing method of the trained obfuscation network isimplemented as the program instructions that can be executed by avariety of the computer components, then computational overhead mayoccur in the computing devices of the buyer when accuracy of the trainedobfuscation network is set as high. Therefore, in accordance with oneexample embodiment of the present disclosure, the buyer is allowed tolower the accuracy to prevent the computational overhead.

The present disclosure has an effect of performing obfuscation, e.g.,anonymization or concealment, in a simple and accurate way, byeliminating a process of searching general data for personalidentification information and a process of obfuscating, e.g.,anonymizing or concealing, the personal identification information.

The present disclosure has another effect of protecting privacy andsecurity of original data by generating obfuscated data, e.g.,anonymized data or concealed data, through irreversibly obfuscating theoriginal data.

The present disclosure has still another effect of generating theobfuscated data recognized as similar or same by computers butrecognized as different by humans.

The present disclosure has still yet another effect of stimulating a bigdata trade market.

The embodiments of the present disclosure as explained above can beimplemented in a form of executable program command through a variety ofcomputer means recordable in computer readable media. The computerreadable media may include solely or in combination, program commands,data files, and data structures. The program commands recorded to themedia may be components specially designed for the present disclosure ormay be usable to those skilled in the art of computer software. Computerreadable media include magnetic media such as hard disk, floppy disk,and magnetic tape, optical media such as CD-ROM and DVD, magneto-opticalmedia such as floptical disk and hardware devices such as ROM, RAM, andflash memory specially designed to store and carry out program commands.Program commands may include not only a machine language code made by acomplier but also a high level code that can be used by an interpreteretc., which may be executed by a computer. The aforementioned hardwaredevice can work as more than a software module to perform the action ofthe present disclosure and vice versa.

As seen above, the present disclosure has been explained by specificmatters such as detailed components, limited embodiments, and drawings.They have been provided only to help more general understanding of thepresent disclosure. It, however, will be understood by those skilled inthe art that various changes and modification may be made from thedescription without departing from the spirit and scope of thedisclosure as defined in the following claims.

Accordingly, the thought of the present disclosure must not be confinedto the explained embodiments, and the following patent claims as well aseverything including variations equal or equivalent to the patent claimspertain to the category of the thought of the present disclosure.

What is claimed is:
 1. A method for training an obfuscation network tobe used for obfuscating original data to protect personal information,comprising steps of: (a) when training data is acquired, a learningdevice performing or supporting another device to perform (i) a processof inputting the training data into an obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate obfuscated training data, and (ii) a process of inputting theobfuscated training data into a compression network, to thereby allowthe compression network to (ii-1) compress the obfuscated training dataand thus generate binary training data and (ii-2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata; (b) the learning device performing or supporting another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data into a learning network having one or more learnedparameters, to thereby allow the learning network to (i-1) apply alearning operation to the compression adaptive obfuscated training databy using the learned parameters and thus (i-2) generate firstcharacteristic information for training corresponding to the compressionadaptive obfuscated training data and (ii) a process of inputting thetraining data into the learning network, to thereby allow the learningnetwork to (ii-1) apply the learning operation to the training data byusing the learned parameters and thus (ii-2) generate secondcharacteristic information for training corresponding to the trainingdata; and (c) the learning device performing or supporting anotherdevice to perform a process of training the obfuscation network suchthat (i) at least one first error, calculated by referring to the firstcharacteristic information for training and the second characteristicinformation for training, is minimized and (ii) at least one seconderror, calculated by referring to (ii-1) (ii-1-a) modified trainingdata, created by modifying the training data, or modified obfuscatedtraining data, created by modifying the obfuscated training data, and(ii-1-b) the obfuscated training data or (ii-2) (ii-2-a) the modifiedtraining data or the modified obfuscated training data and (ii-2-b) thecompression adaptive obfuscated training data, is maximized; wherein thelearning network includes a first learning network to an n-th learningnetwork respectively having one or more first learned parameters to oneor more n-th learned parameters wherein n is an integer greater than 0,wherein, at the step of (b), the learning device performs or supportsanother device to perform (i) a process of inputting the compressionadaptive obfuscated training data into each of the first learningnetwork to the n-th learning network, to thereby allow each of the firstlearning network to the n-th learning network to (i-1) apply itscorresponding learning operation to the compression adaptive obfuscatedtraining data by using respectively the first learned parameters to then-th learned parameters of the first learning network to the n-thlearning network, and thus (i-2) output each piece of (1_1)-stcharacteristic information for training to (1_n)-th characteristicinformation for training on the compression adaptive obfuscated trainingdata and (ii) a process of inputting the training data into each of thefirst learning network to the n-th learning network, to thereby alloweach of the first learning network to the n-th learning network to(ii-1) apply its corresponding learning operation to the training databy using respectively the first learned parameters to the n-th learnedparameters, and thus (ii-2) output each piece of (2_1)-st characteristicinformation for training to (2_n)-th characteristic information fortraining on the training data, and wherein, at the step of (c), thelearning device performs or supports another device to perform a processof training the obfuscation network such that the first error, which isan average over (1) a (1_1)-st error calculated by referring to the(1_1)-st characteristic information for training and the (2_1)-stcharacteristic information for training to (2) a (1_n)-th errorcalculated by referring to the (1_n)-th characteristic information fortraining and the (2_n)-th characteristic information for training, isminimized and such that the second error is maximized.
 2. The method ofclaim 1, wherein, at the step of (c), the learning device performs orsupports another device to perform a process of calculating the firsterror by referring to a difference between the first characteristicinformation for training and the second characteristic information fortraining and a process of calculating the second error by referring to(1) a difference between (1-a) the modified training data or themodified obfuscated training data and (1-b) the obfuscated training dataor (2) a difference between (2-a) the modified training data or themodified obfuscated training data and (2-b) the compression adaptiveobfuscated training data.
 3. The method of claim 2, wherein the learningdevice performs or supports another device to perform a process ofacquiring the first error by referring to a norm or a cosine similaritybetween the first characteristic information for training and the secondcharacteristic information for training.
 4. The method of claim 1,wherein, at the step of (c), the learning device performs or supportsanother device to perform a process of calculating the first error byfurther referring to at least one class loss which is calculated byreferring to (1) each of probabilities that each piece of the firstcharacteristic information for training, each piece of which is mappedonto each class, belongs to its corresponding class and (2) a groundtruth corresponding to the training data.
 5. The method of claim 1,wherein, at the step of (c), the learning device performs or supportsanother device to perform a process of measuring at least one quality byreferring to at least part of an entropy of the compression adaptiveobfuscated training data and a degree of noise of the compressionadaptive obfuscated training data and a process of acquiring the firsterror by further referring to the measured quality.
 6. A method fortraining an obfuscation network to be used for obfuscating original datato protect personal information, comprising steps of: (a) when trainingdata is acquired, a learning device performing or supporting anotherdevice to perform (i) a process of inputting the training data into anobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate obfuscated trainingdata, and (ii) a process of inputting the obfuscated training data intoa compression network, to thereby allow the compression network to(ii-1) compress the obfuscated training data and thus generate binarytraining data and (ii-2) decompress the binary training data and thusgenerate compression adaptive obfuscated training data; (b) the learningdevice performing or supporting another device to perform (i) a processof inputting the compression adaptive obfuscated training data into alearning network having one or more learned parameters, to thereby allowthe learning network to (i-1) apply a learning operation to thecompression adaptive obfuscated training data by using the learnedparameters and thus (i-2) generate first characteristic information fortraining corresponding to the compression adaptive obfuscated trainingdata and (ii) a process of inputting the training data into the learningnetwork, to thereby allow the learning network to (ii-1) apply thelearning operation to the training data by using the learned parametersand thus (ii-2) generate second characteristic information for trainingcorresponding to the training data; and (c) the learning deviceperforming or supporting another device to perform a process of trainingthe obfuscation network such that (i) at least one first error,calculated by referring to the first characteristic information fortraining and the second characteristic information for training, isminimized and (ii) at least one second error, calculated by referring to(ii-1) (ii-1-a) modified training data, created by modifying thetraining data, or modified obfuscated training data, created bymodifying the obfuscated training data, and (ii-1-b) the obfuscatedtraining data or (ii-2) (ii-2-a) the modified training data or themodified obfuscated training data and (ii-2-b) the compression adaptiveobfuscated training data, is maximized; wherein the learning networkincludes a first learning network to an n-th learning networkrespectively having one or more first learned parameters to one or moren-th learned parameters wherein n is an integer greater than 0, wherein,at the step of (a), the learning device performs or supports anotherdevice to perform (i) a process of inputting the training data into theobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate first obfuscatedtraining data and (ii) a process of inputting the first obfuscatedtraining data into the compression network, to thereby allow thecompression network to (ii-1) compress the first obfuscated trainingdata and thus generate first binary training data and (ii-2) decompressthe first binary training data and thus generate first compressionadaptive obfuscated training data, wherein, at the step of (b), thelearning device performs or supports another device to perform (i) aprocess of inputting the first compression adaptive obfuscated trainingdata into the first learning network, to thereby allow the firstlearning network to (i-1) apply the learning operation to the firstcompression adaptive obfuscated training data by using the first learnedparameters of the first learning network and thus (i-2) output (1_1)-stcharacteristic information for training on the first compressionadaptive obfuscated training data and (ii) a process of inputting thetraining data into the first learning network, to thereby allow thefirst learning network to (ii-1) apply the learning operation to thetraining data by using the first learned parameters and thus (ii-2)output (2_1)-st characteristic information for training on the trainingdata, wherein, at the step of (c), the learning device performs orsupports another device to perform a process of training the obfuscationnetwork such that (i) at least one (1_1)-st error, calculated byreferring to the (1_1)-st characteristic information for training andthe (2_1)-st characteristic information for training, is minimized and(ii) at least one (2_1)-st error, calculated by referring to (ii-1)(ii-1-a) the modified training data or first modified obfuscatedtraining data created by modifying the first obfuscated training dataand (ii-1-b) the first obfuscated training data or (ii-2) (ii-2-a) themodified training data or the first modified obfuscated training dataand (ii-2-b) the first compression adaptive obfuscated training data, ismaximized, to thereby allow the obfuscation network to be a firsttrained obfuscation network, and wherein, while increasing an integer kfrom 2 to n, the learning device performs or supports another device toperform (i) a process of inputting the training data into the (k-1)-thtrained obfuscation network, to thereby allow the (k-1)-th trainedobfuscation network to obfuscate the training data and thus to generatek-th obfuscated training data and a process of inputting the k-thobfuscated training data into the compression network, to thereby allowthe compression network to (1) compress the k-th obfuscated trainingdata and thus generate k-th binary training data and (2) decompress thek-th binary training data and thus generate k-th compression adaptiveobfuscated training data, (ii) (ii-1) a process of inputting the k-thcompression adaptive obfuscated training data into a k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the k-th compression adaptive obfuscated trainingdata by using one or more k-th learned parameters of the k-th learningnetwork and thus to output (1_k)-th characteristic information fortraining on the k-th compression adaptive obfuscated training data and(ii-2) a process of inputting the training data into the k-th learningnetwork, to thereby allow the k-th learning network to apply thelearning operation to the training data by using the k-th learnedparameters and thus to output (2_k)-th characteristic information fortraining on the training data, and (iii) a process of training the(k-1)-th trained obfuscation network such that at least one (1_k)-therror, calculated by referring to the (1_k)-th characteristicinformation for training and the (2_k)-th characteristic information fortraining, is minimized and such that at least one (2_k)-th error, whichis calculated by referring to (iii-1) (iii-1-a) the modified trainingdata or k-th modified obfuscated training data calculated by modifyingthe k-th obfuscated training data and (iii-1-b) the k-th obfuscatedtraining data or (iii-2) (iii-2-a) the modified training data or thek-th modified obfuscated training data and (iii-2-b) the k-thcompression adaptive obfuscated training data, is maximized, to therebyallow the (k-1)-th trained obfuscation network to be a k-th trainedobfuscation network.
 7. A method for training an obfuscation network tobe used for obfuscating original data to protect personal information,comprising steps of: (a) when training data is acquired, a learningdevice performing or supporting another device to perform (i) a processof inputting the training data into an obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate obfuscated training data, and (ii) a process of inputting theobfuscated training data into a compression network, to thereby allowthe compression network to (ii-1) compress the obfuscated training dataand thus generate binary training data and (ii-2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata; (b) the learning device performing or supporting another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data into a learning network having one or more learnedparameters, to thereby allow the learning network to (i-1) apply alearning operation to the compression adaptive obfuscated training databy using the learned parameters and thus (i-2) generate firstcharacteristic information for training corresponding to the compressionadaptive obfuscated training data and (ii) a process of inputting thetraining data into the learning network, to thereby allow the learningnetwork to (ii-1) apply the learning operation to the training data byusing the learned parameters and thus (ii-2) generate secondcharacteristic information for training corresponding to the trainingdata; and (c) the learning device performing or supporting anotherdevice to perform a process of training the obfuscation network suchthat (i) at least one first error, calculated by referring to the firstcharacteristic information for training and the second characteristicinformation for training, is minimized and (ii) at least one seconderror, calculated by referring to (ii-1) (ii-1-a) modified trainingdata, created by modifying the training data, or modified obfuscatedtraining data, created by modifying the obfuscated training data, and(ii-1-b) the obfuscated training data or (ii-2) (ii-2-a) the modifiedtraining data or the modified obfuscated training data and (ii-2-b) thecompression adaptive obfuscated training data, is maximized; wherein, atthe step of (c), on condition that an obfuscated training data score,corresponding to the obfuscated training data inputted into adiscriminator capable of determining whether its inputted data is realor fake or the compression adaptive obfuscated training data inputtedinto the discriminator, has been acquired as the second error, thelearning device performs or supports another device to perform a processof training the obfuscation network such that the first error isminimized and the second error is maximized and a process of trainingthe discriminator such that a training data score, corresponding to themodified training data inputted into the discriminator or the modifiedobfuscated training data inputted into the discriminator, is maximizedand such that the obfuscated training data score is minimized; whereinthe learning network includes a first learning network to an n-thlearning network respectively having one or more first learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the step of (b), the learning deviceperforms or supports another device to perform (i) a process ofinputting the compression adaptive obfuscated training data into each ofthe first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into each of the first learning network to the n-th learningnetwork, to thereby allow each of the first learning network to the n-thlearning network to (ii-1) apply its corresponding learning operation tothe training data by using respectively the first learned parameters tothe n-th learned parameters, and thus (ii-2) output each piece of(2_1)-st characteristic information for training to (2_n)-thcharacteristic information for training on the training data, andwherein, at the step of (c), the learning device performs or supportsanother device to perform (i) a process of training the obfuscationnetwork such that the first error, which is an average over (i-1) atleast one (1_1)-st error calculated by referring to the (1_1)-stcharacteristic information for training and the (2_1)-st characteristicinformation for training to (i-2) at least one (1_n)-th error calculatedby referring to the (1_n)-th characteristic information for training andthe (2_n)-th characteristic information for training, is minimized andsuch that the second error, which is the obfuscated training data score,corresponding to the obfuscated training data inputted into thediscriminator or the compression adaptive obfuscated training datainputted into the discriminator, is maximized and (ii) a process oftraining the discriminator such that the training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized.
 8. A method for training an obfuscation network tobe used for obfuscating original data to protect personal information,comprising steps of: (a) when training data is acquired, a learningdevice performing or supporting another device to perform (i) a processof inputting the training data into an obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate obfuscated training data, and (ii) a process of inputting theobfuscated training data into a compression network, to thereby allowthe compression network to (ii-1) compress the obfuscated training dataand thus generate binary training data and (ii-2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata; (b) the learning device performing or supporting another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data into a learning network having one or more learnedparameters, to thereby allow the learning network to (i-1) apply alearning operation to the compression adaptive obfuscated training databy using the learned parameters and thus (i-2) generate firstcharacteristic information for training corresponding to the compressionadaptive obfuscated training data and (ii) a process of inputting thetraining data into the learning network, to thereby allow the learningnetwork to (ii-1) apply the learning operation to the training data byusing the learned parameters and thus (ii-2) generate secondcharacteristic information for training corresponding to the trainingdata; and (c) the learning device performing or supporting anotherdevice to perform a process of training the obfuscation network suchthat (i) at least one first error, calculated by referring to the firstcharacteristic information for training and the second characteristicinformation for training, is minimized and (ii) at least one seconderror, calculated by referring to (ii-1) (ii-1-a) modified trainingdata, created by modifying the training data, or modified obfuscatedtraining data, created by modifying the obfuscated training data, and(ii-1-b) the obfuscated training data or (ii-2) (ii-2-a) the modifiedtraining data or the modified obfuscated training data and (ii-2-b) thecompression adaptive obfuscated training data, is maximized; wherein, atthe step of (c), on condition that an obfuscated training data score,corresponding to the obfuscated training data inputted into adiscriminator capable of determining whether its inputted data is realor fake or the compression adaptive obfuscated training data inputtedinto the discriminator, has been acquired as the second error, thelearning device performs or supports another device to perform a processof training the obfuscation network such that the first error isminimized and the second error is maximized and a process of trainingthe discriminator such that a training data score, corresponding to themodified training wherein the learning network includes a first learningnetwork to an n-th learning network respectively having one or morefirst learned parameters to one or more n-th learned parameters whereinn is an integer greater than 0, wherein, at the step of (a), thelearning device performs or supports another device to perform (i) aprocess of inputting the training data into the obfuscation network, tothereby allow the obfuscation network to obfuscate the training data andthus to generate first obfuscated training data and (ii) a process ofinputting the first obfuscated training data into the compressionnetwork, to thereby allow the compression network to (ii-1) compress thefirst obfuscated training data and thus generate first binary trainingdata and (ii-2) decompress the first binary training data and thusgenerate first compression adaptive obfuscated training data, wherein,at the step of (b), the learning device performs or supports anotherdevice to perform (i) a process of inputting the first compressionadaptive obfuscated training data into the first learning network, tothereby allow the first learning network to (i-1) apply the learningoperation to the first compression adaptive obfuscated training data byusing the first learned parameters of the first learning network andthus (i-2) output (1_1)-st characteristic information for training onthe first compression adaptive obfuscated training data and (ii) aprocess of inputting the training data into the first learning network,to thereby allow the first learning network to (ii-1) apply the learningoperation to the training data by using the first learned parameters andthus (ii-2) output (2_1)-st characteristic information for training onthe training data, wherein, at the step of (c), the learning deviceperforms or supports another device to perform (i) a process of trainingthe obfuscation network such that at least one (1_1)-st error,calculated by referring to the (1_1)-st characteristic information fortraining and the (2_1)-st characteristic information for training, isminimized and such that at least one (2_1)-st error, which is a firstobfuscated training data score, corresponding to the first obfuscatedtraining data inputted into the discriminator or the first compressionadaptive obfuscated training data inputted into the discriminator, ismaximized, to thereby allow the obfuscation network to be a firsttrained obfuscation network and (ii) a process of training thediscriminator such that a first training data score, corresponding tothe modified training data inputted into the discriminator or firstmodified obfuscated training data inputted into the discriminator, ismaximized and such that the first obfuscated training data score isminimized, to thereby allow the discriminator to be a first traineddiscriminator, wherein the first modified obfuscated training data iscreated by modifying the first obfuscated training data and wherein,while increasing an integer k from 2 to n, the learning device performsor supports another device to perform (i) a process of inputting thetraining data into the (k-1)-th trained obfuscation network, to therebyallow the (k-1)-th trained obfuscation network to obfuscate the trainingdata and thus to generate k-th obfuscated training data and a process ofinputting the k-th obfuscated training data into the compressionnetwork, to thereby allow the compression network to (1) compress thek-th obfuscated training data and thus generate k-th binary trainingdata and (2) decompress the k-th binary training data and thus generatek-th compression adaptive obfuscated training data, (ii) (ii-1) aprocess of inputting the k-th compression adaptive obfuscated trainingdata into a k-th learning network, to thereby allow the k-th learningnetwork to apply the learning operation to the k-th compression adaptiveobfuscated training data by using one or more k-th learned parameters ofthe k-th learning network and thus to output (1_k)-th characteristicinformation for training on the k-th compression adaptive obfuscatedtraining data and (ii-2) a process of inputting the training data intothe k-th learning network, to thereby allow the k-th learning network toapply the learning operation to the training data by using the k-thlearned parameters and thus to output (2_k)-th characteristicinformation for training on the training data, and (iii) (iii-1) aprocess of training the (k-1)-th trained obfuscation network such thatat least one (1_k)-th error, calculated by referring to the (1_k)-thcharacteristic information for training and the (2_k)-th characteristicinformation for training, is minimized and such that at least one(2_k)-th error, which is a k-th obfuscated training data score,corresponding to the k-th obfuscated training data inputted into a(k-1)-th trained discriminator or the k-th compression adaptiveobfuscated training data inputted into the (k-1)-th traineddiscriminator, is maximized, to thereby allow the (k-1)-th trainedobfuscation network to be a k-th trained obfuscation network and (iii-2)a process of training the (k-1)-th trained discriminator such that ak-th training data score, corresponding to the modified training datainputted into the (k-1)-th trained discriminator or k-th modifiedobfuscated training data inputted into the (k-1)-th traineddiscriminator, is maximized and such that the k-th obfuscated trainingdata is minimized, to thereby allow the (k-1)-th trained discriminatorto be a k-th trained discriminator, wherein the k-th modified obfuscatedtraining data is created by modifying the k-th obfuscated training data.9. A method for training an obfuscation network to be used forobfuscating original data to protect personal information, comprisingsteps of: (a) when training data is acquired, a learning deviceperforming or supporting another device to perform (i) a process ofinputting the training data into an obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate obfuscated training data, and (ii) a process of inputting theobfuscated training data into a compression network, to thereby allowthe compression network to (ii-1) compress the obfuscated training dataand thus generate binary training data and (ii-2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata; (b) the learning device performing or supporting another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data into a learning network having one or more learnedparameters, to thereby allow the learning network to (i-1) apply alearning operation to the compression adaptive obfuscated training databy using the learned parameters and thus (i-2) generate firstcharacteristic information for training corresponding to the compressionadaptive obfuscated training data and (ii) a process of inputting thetraining data into the learning network, to thereby allow the learningnetwork to (ii-1) apply the learning operation to the training data byusing the learned parameters and thus (ii-2) generate secondcharacteristic information for training corresponding to the trainingdata; and (c) the learning device performing or supporting anotherdevice to perform a process of training the obfuscation network suchthat (i) at least one first error, calculated by referring to the firstcharacteristic information for training and the second characteristicinformation for training, is minimized and (ii) at least one seconderror, calculated by referring to (ii-1) (ii-1-a) modified trainingdata, created by modifying the training data, or modified obfuscatedtraining data, created by modifying the obfuscated training data, and(ii-1-b) the obfuscated training data or (ii-2) (ii-2-a) the modifiedtraining data or the modified obfuscated training data and (ii-2-b) thecompression adaptive obfuscated training data, is maximized; wherein, atthe step of (c), on condition that an obfuscated training data score,corresponding to the obfuscated training data inputted into adiscriminator capable of determining whether its inputted data is realor fake or the compression adaptive obfuscated training data inputtedinto the discriminator, has been acquired as the second error, thelearning device performs or supports another device to perform a processof training the obfuscation network such that the first error isminimized and the second error is maximized and a process of trainingthe discriminator such that a training data score, corresponding to themodified training wherein a maximum of the training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is 1 as a value for determining the modified trainingdata or the modified obfuscated training data as real and wherein aminimum of the obfuscated training data score, corresponding to theobfuscated training data inputted into the discriminator or thecompression adaptive obfuscated training data inputted into thediscriminator, is 0 as a value for determining the obfuscated trainingdata or the compression adaptive obfuscated training data as fake.
 10. Amethod for testing an obfuscation network to be used for obfuscatingoriginal data to protect personal information, comprising steps of: atesting device, on condition that the learning device has performed orsupported another device to perform (i) a process of inputting trainingdata into an obfuscation network, to thereby allow the obfuscationnetwork to obfuscate the training data and thus to generate obfuscatedtraining data and a process of inputting the obfuscated training datainto a compression network, to thereby allow the compression network to(1) compress the obfuscated training data and thus generate binarytraining data and (2) decompress the binary training data and thusgenerate compression adaptive obfuscated training data, (ii) (ii-1) aprocess of inputting the compression adaptive obfuscated training datainto a learning network having one or more learned parameters, tothereby allow the learning network to apply a learning operation to thecompression adaptive obfuscated training data by using the learnedparameters and thus to output first characteristic information fortraining on the compression adaptive obfuscated training data and (ii-2)a process of inputting the training data into the learning network, tothereby allow the learning network to apply the learning operation tothe training data by using the learned parameters and thus to outputsecond characteristic information for training on the training data, and(iii) a process of training the obfuscation network such that at leastone first error, calculated by referring to the first characteristicinformation for training and the second characteristic information fortraining, is minimized and such that at least one second error, which iscalculated by referring to (iii-1) (iii-1-a) modified training data,created by modifying the training data, or modified obfuscated trainingdata, created by modifying the obfuscated training data, and (iii-1-b)the obfuscated training data or (iii-2) (iii-2-a) the modified trainingdata or the modified obfuscated training data and (iii-2-b) thecompression adaptive obfuscated training data, is maximized, performingor supporting another device to perform a process of acquiring testdata; and (b) the testing device performing or supporting another deviceto perform a process of inputting the test data into the obfuscationnetwork, to thereby allow the obfuscation network to obfuscate the testdata by using the learned parameters of the obfuscation network and thusto output obfuscated test data; wherein, at the step of (a), thelearning network includes a first learning network to an n-th learningnetwork respectively having one or more first parameters to one or moren-th learned parameters wherein n is an integer greater than 0, andwherein the learning device has performed or supported another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data into each of the first learning network to the n-thlearning network, to thereby allow each of the first learning network tothe n-th learning network to (i-1) apply its corresponding learningoperation to the compression adaptive obfuscated training data by usingrespectively the first learned parameters to the n-th learned parametersof the first learning network to the n-th learning network, and thus(i-2) output each piece of (1_1)-st characteristic information fortraining to (1_n)-th characteristic information for training on thecompression adaptive obfuscated training data, (ii) a process ofinputting the training data into each of the first learning network tothe n-th learning network, to thereby allow each of the first learningnetwork to the n-th learning network to (ii-1) apply its correspondinglearning operation to the training data by using respectively the firstlearned parameters to the n-th learned parameters, and thus (ii-2)output each piece of (2_1)-st characteristic information for training to(2_n)-th characteristic information for training on the training data,and (iii) a process of training the obfuscation network such that thefirst error is minimized which is an average over (iii-1) the (1_1)-sterror acquired by referring to the (1_1)-st characteristic informationfor training and the (2_1)-st characteristic information for training to(iii-2) the (1_n)-th error acquired by referring to the (1_n)-thcharacteristic information for training and the (2_n)-th characteristicinformation for training and such that the second error is maximizedwhich is calculated by referring to (iii-3) (iii-3-a) the modifiedtraining data or the modified obfuscated training data and (iii-3-b) theobfuscated training data or (iii-4) (iii-4-a) the modified training dataor the modified obfuscated training data and (iii-4-b) the compressionadaptive obfuscated training data.
 11. A method for testing anobfuscation network to be used for obfuscating original data to protectpersonal information, comprising steps of: a testing device, oncondition that the learning device has performed or supported anotherdevice to perform (i) a process of inputting training data into anobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate obfuscated trainingdata and a process of inputting the obfuscated training data into acompression network, to thereby allow the compression network to (1)compress the obfuscated training data and thus generate binary trainingdata and (2) decompress the binary training data and thus generatecompression adaptive obfuscated training data, (ii) (ii-1) a process ofinputting the compression adaptive obfuscated training data into alearning network having one or more learned parameters, to thereby allowthe learning network to apply a learning operation to the compressionadaptive obfuscated training data by using the learned parameters andthus to output first characteristic information for training on thecompression adaptive obfuscated training data and (ii-2) a process ofinputting the training data into the learning network, to thereby allowthe learning network to apply the learning operation to the trainingdata by using the learned parameters and thus to output secondcharacteristic information for training on the training data, and (iii)a process of training the obfuscation network such that at least onefirst error, calculated by referring to the first characteristicinformation for training and the second characteristic information fortraining, is minimized and such that at least one second error, which iscalculated by referring to (iii-1) (iii-1-a) modified training data,created by modifying the training data, or modified obfuscated trainingdata, created by modifying the obfuscated training data, and (iii-1-b)the obfuscated training data or (iii-2) (iii-2-a) the modified trainingdata or the modified obfuscated training data and (iii-2-b) thecompression adaptive obfuscated training data, is maximized, performingor supporting another device to perform a process of acquiring testdata; and (b) the testing device performing or supporting another deviceto perform a process of inputting the test data into the obfuscationnetwork, to thereby allow the obfuscation network to obfuscate the testdata by using the learned parameters of the obfuscation network and thusto output obfuscated test data; wherein, at the step of (a), uponacquiring an obfuscated training data score, as the second error,corresponding to the obfuscated training data inputted into adiscriminator capable of determining whether its inputted data is realor fake or the compression adaptive obfuscated training data inputtedinto the discriminator, the learning device has performed or supportedanother device to perform a process of training the obfuscation networksuch that the first error is minimized and the second error is maximizedand a process of training the discriminator such that a training datascore, corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized; wherein, at the step of (a), the learning networkincludes a 1-st learning network to an n-th learning networkrespectively having one or more 1-st learned parameters to one or moren-th learned parameters wherein n is an integer greater than 0, andwherein the learning device has performed or supported another device toperform (i) a process of inputting the compression adaptive obfuscatedtraining data into each of the first learning network to the n-thlearning network, to thereby allow each of the first learning network tothe n-th learning network to (i-1) apply its corresponding learningoperation to the compression adaptive obfuscated training data by usingrespectively the first learned parameters to the n-th learned parametersof the first learning network to the n-th learning network, and thus(i-2) output each piece of (1_1)-st characteristic information fortraining to (1_n)-th characteristic information for training on thecompression adaptive obfuscated training data, (ii) a process ofinputting the training data into each of the first learning network tothe n-th learning network, to thereby allow each of the first learningnetwork to the n-th learning network to (ii-1) apply its correspondinglearning operation to the training data by using respectively the firstlearned parameters to the n-th learned parameters, and thus (ii-2)output each piece of (2_1)-st characteristic information for training to(2_n)-th characteristic information for training on the training data,and (iii) a process of training the obfuscation network such that thefirst error is minimized which is an average over (iii-1) the (1_1)-sterror acquired by referring to the (1_1)-st characteristic informationfor training and the (2_1)-st characteristic information for training to(iii-2) the (1_n)-th error acquired by referring to the (1_n)-thcharacteristic information for training and the (2_n)-th characteristicinformation for training and such that the second error which is theobfuscated training data score, corresponding to the obfuscated trainingdata inputted into the discriminator or the compression adaptiveobfuscated training data inputted into the discriminator, is maximizedand (iv) a process of training the discriminator such that the trainingdata score, corresponding to the modified training data inputted intothe discriminator or the modified obfuscated training data inputted intothe discriminator, is maximized and such that the obfuscated trainingdata score is minimized.
 12. A learning device for training anobfuscation network to be used for obfuscating original data to protectpersonal information, comprising: at least one memory that storesinstructions; and at least one processor configured to execute theinstructions to perform or support another device to perform: (I) whentraining data is acquired, (i) a process of inputting the training datainto an obfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate obfuscated trainingdata, and (ii) a process of inputting the obfuscated training data intoa compression network, to thereby allow the compression network to(ii-1) compress the obfuscated training data and thus generate binarytraining data and (ii-2) decompress the binary training data and thusgenerate compression adaptive obfuscated training data, (II) (i) aprocess of inputting the compression adaptive obfuscated training datainto a learning network having one or more learned parameters, tothereby allow the learning network to (i-1) apply a learning operationto the compression adaptive obfuscated training data by using thelearned parameters and thus (i-2) generate first characteristicinformation for training corresponding to the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into the learning network, to thereby allow the learning network to(ii-1) apply the learning operation to the training data by using thelearned parameters and thus (ii-2) generate second characteristicinformation for training corresponding to the training data, and (III) aprocess of training the obfuscation network such that (i) at least onefirst error, calculated by referring to the first characteristicinformation for training and the second characteristic information fortraining, is minimized and (ii) at least one second error, calculated byreferring to (ii-1) (ii-1-a) modified training data, created bymodifying the training data, or modified obfuscated training data,created by modifying the obfuscated training data, and (ii-1-b) theobfuscated training data or (ii-2) (ii-2-a) the modified training dataor the modified obfuscated training data and (ii-2-b) the compressionadaptive obfuscated training data, is maximized; wherein the learningnetwork includes a first learning network to an n-th learning networkrespectively having one or more first learned parameters to one or moren-th learned parameters wherein n is an integer greater than 0, wherein,at the process of (II), the processor performs or supports anotherdevice to perform (i) a process of inputting the compression adaptiveobfuscated training data into each of the first learning network to then-th learning network, to thereby allow each of the first learningnetwork to the n-th learning network to (i-1) apply its correspondinglearning operation to the compression adaptive obfuscated training databy using respectively the first learned parameters to the n-th learnedparameters of the first learning network to the n-th learning network,and thus (i-2) output each piece of (1_1)-st characteristic informationfor training to (1_n)-th characteristic information for training on thecompression adaptive obfuscated training data and (ii) a process ofinputting the training data into each of the first learning network tothe n-th learning network, to thereby allow each of the first learningnetwork to the n-th learning network to (ii-1) apply its correspondinglearning operation to the training data by using respectively the firstlearned parameters to the n-th learned parameters, and thus (ii-2)output each piece of (2_1)-st characteristic information for training to(2_n)-th characteristic information for training on the training data,and wherein, at the process of (III), the processor performs or supportsanother device to perform a process of training the obfuscation networksuch that the first error, which is an average over (1) a (1_1)-st errorcalculated by referring to the (1_1)-st characteristic information fortraining and the (2_1)-st characteristic information for training to (2)a (1_n)-th error calculated by referring to the (1_n)-th characteristicinformation for training and the (2_n)-th characteristic information fortraining, is minimized and such that the second error is maximized. 13.The learning device of claim 12, wherein, at the process of (III), theprocessor performs or supports another device to perform a process ofcalculating the first error by referring to a difference between thefirst characteristic information for training and the secondcharacteristic information for training and a process of calculating thesecond error by referring to (1) a difference between (1-a) the modifiedtraining data or the modified obfuscated training data and (1-b) theobfuscated training data or (2) a difference between (2-a) the modifiedtraining data or the modified obfuscated training data and (2-b) thecompression adaptive obfuscated training data.
 14. The learning deviceof claim 13, wherein the processor performs or supports another deviceto perform a process of acquiring the first error by referring to a normor a cosine similarity between the first characteristic information fortraining and the second characteristic information for training.
 15. Thelearning device of claim 12, wherein, at the process of (III), theprocessor performs or supports another device to perform a process ofcalculating the first error by further referring to at least one classloss which is calculated by referring to (1) each of probabilities thateach piece of the first characteristic information for training, eachpiece of which is mapped onto each class, belongs to its correspondingclass and (2) a ground truth corresponding to the training data.
 16. Thelearning device of claim 12, wherein, at the process of (III), theprocessor performs or supports another device to perform a process ofmeasuring at least one quality by referring to at least part of anentropy of the compression adaptive obfuscated training data and adegree of noise of the compression adaptive obfuscated training data anda process of acquiring the first error by further referring to themeasured quality.
 17. A learning device for training an obfuscationnetwork to be used for obfuscating original data to protect personalinformation, comprising: at least one memory that stores instructions;and at least one processor configured to execute the instructions toperform or support another device to perform: (I) when training data isacquired, (i) a process of inputting the training data into anobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate obfuscated trainingdata, and (ii) a process of inputting the obfuscated training data intoa compression network, to thereby allow the compression network to(ii-1) compress the obfuscated training data and thus generate binarytraining data and (ii-2) decompress the binary training data and thusgenerate compression adaptive obfuscated training data, (II) (i) aprocess of inputting the compression adaptive obfuscated training datainto a learning network having one or more learned parameters, tothereby allow the learning network to (i-1) apply a learning operationto the compression adaptive obfuscated training data by using thelearned parameters and thus (i-2) generate first characteristicinformation for training corresponding to the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into the learning network, to thereby allow the learning network to(ii-1) apply the learning operation to the training data by using thelearned parameters and thus (ii-2) generate second characteristicinformation for training corresponding to the training data, and (III) aprocess of training the obfuscation network such that (i) at least onefirst error, calculated by referring to the first characteristicinformation for training and the second characteristic information fortraining, is minimized and (ii) at least one second error, calculated byreferring to (ii-1) (ii-1-a) modified training data, created bymodifying the training data, or modified obfuscated training data,created by modifying the obfuscated training data, and (ii-1-b) theobfuscated training data or (ii-2) (ii-2-a) the modified training dataor the modified obfuscated training data and (ii-2-b) the compressionadaptive obfuscated training data, is maximized; wherein the learningnetwork includes a first learning network to an n-th learning networkrespectively having one or more first learned parameters to one or moren-th learned parameters wherein n is an integer greater than 0; whereinthe learning network includes a first learning network to an n-thlearning network respectively having one or more first learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the process of (I), the processorperforms or supports another device to perform (i) a process ofinputting the training data into the obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate first obfuscated training data and (ii) a process of inputtingthe first obfuscated training data into the compression network, tothereby allow the compression network to (ii-1) compress the firstobfuscated training data and thus generate first binary training dataand (ii-2) decompress the first binary training data and thus generatefirst compression adaptive obfuscated training data, wherein, at theprocess of (II), the processor performs or supports another device toperform (i) a process of inputting the first compression adaptiveobfuscated training data into the first learning network, to therebyallow the first learning network to (i-1) apply the learning operationto the first compression adaptive obfuscated training data by using thefirst learned parameters of the first learning network and thus (i-2)output (1_1)-st characteristic information for training on the firstcompression adaptive obfuscated training data and (ii) a process ofinputting the training data into the first learning network, to therebyallow the first learning network to (ii-1) apply the learning operationto the training data by using the first learned parameters and thus(ii-2) output (2_1)-st characteristic information for training on thetraining data, wherein, at the process of (III), the processor performsor supports another device to perform a process of training theobfuscation network such that (i) at least one (1_1)-st error,calculated by referring to the (1_1)-st characteristic information fortraining and the (2_1)-st characteristic information for training, isminimized and (ii) at least one (2_1)-st error, calculated by referringto (ii-1) (ii-1-a) the modified training data or first modifiedobfuscated training data created by modifying the first obfuscatedtraining data and (ii-1-b) the first obfuscated training data or (ii-2)(ii-2-a) the modified training data or the first modified obfuscatedtraining data and (ii-2-b) the first compression adaptive obfuscatedtraining data, is maximized, to thereby allow the obfuscation network tobe a first trained obfuscation network, and wherein, while increasing aninteger k from 2 to n, the processor performs or supports another deviceto perform (i) a process of inputting the training data into the(k-1)-th trained obfuscation network, to thereby allow the (k-1)-thtrained obfuscation network to obfuscate the training data and thus togenerate k-th obfuscated training data and a process of inputting thek-th obfuscated training data into the compression network, to therebyallow the compression network to (1) compress the k-th obfuscatedtraining data and thus generate k-th binary training data and (2)decompress the k-th binary training data and thus generate k-thcompression adaptive obfuscated training data, (ii) (ii-1) a process ofinputting the k-th compression adaptive obfuscated training data into ak-th learning network, to thereby allow the k-th learning network toapply the learning operation to the k-th compression adaptive obfuscatedtraining data by using one or more k-th learned parameters of the k-thlearning network and thus to output (1_k)-th characteristic informationfor training on the k-th compression adaptive obfuscated training dataand (ii-2) a process of inputting the training data into the k-thlearning network, to thereby allow the k-th learning network to applythe learning operation to the training data by using the k-th learnedparameters and thus to output (2_k)-th characteristic information fortraining on the training data, and (iii) a process of training the(k-1)-th trained obfuscation network such that at least one (1_k)-therror, calculated by referring to the (1_k)-th characteristicinformation for training and the (2_k)-th characteristic information fortraining, is minimized and such that at least one (2_k)-th error, whichis calculated by referring to (iii-1) (iii-1-a) the modified trainingdata or k-th modified obfuscated training data calculated by modifyingthe k-th obfuscated training data and (iii-1-b) the k-th obfuscatedtraining data or (iii-2) (iii-2-a) the modified training data or thek-th modified obfuscated training data and (iii-2-b) the k-thcompression adaptive obfuscated training data, is maximized, to therebyallow the (k-1)-th trained obfuscation network to be a k-th trainedobfuscation network.
 18. A learning device for training an obfuscationnetwork to be used for obfuscating original data to protect personalinformation, comprising: at least one memory that stores instructions;and at least one processor configured to execute the instructions toperform or support another device to perform: (I) when training data isacquired, (i) a process of inputting the training data into anobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate obfuscated trainingdata, and (ii) a process of inputting the obfuscated training data intoa compression network, to thereby allow the compression network to(ii-1) compress the obfuscated training data and thus generate binarytraining data and (ii-2) decompress the binary training data and thusgenerate compression adaptive obfuscated training data, (II) (i) aprocess of inputting the compression adaptive obfuscated training datainto a learning network having one or more learned parameters, tothereby allow the learning network to (i-1) apply a learning operationto the compression adaptive obfuscated training data by using thelearned parameters and thus (i-2) generate first characteristicinformation for training corresponding to the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into the learning network, to thereby allow the learning network to(ii-1) apply the learning operation to the training data by using thelearned parameters and thus (ii-2) generate second characteristicinformation for training corresponding to the training data, and (III) aprocess of training the obfuscation network such that (i) at least onefirst error, calculated by referring to the first characteristicinformation for training and the second characteristic information fortraining, is minimized and (ii) at least one second error, calculated byreferring to (ii-1) (ii-1-a) modified training data, created bymodifying the training data, or modified obfuscated training data,created by modifying the obfuscated training data, and (ii-1-b) theobfuscated training data or (ii-2) (ii-2-a) the modified training dataor the modified obfuscated training data and (ii-2-b) the compressionadaptive obfuscated training data, is maximized; wherein the learningnetwork includes a first learning network to an n-th learning networkrespectively having one or more first learned parameters to one or moren-th learned parameters wherein n is an integer greater than 0; wherein,at the process of (III), on condition that an obfuscated training datascore, corresponding to the obfuscated training data inputted into adiscriminator capable of determining whether its inputted data is realor fake or the compression adaptive obfuscated training data inputtedinto the discriminator, has been acquired as the second error, theprocessor performs or supports another device to perform a process oftraining the obfuscation network such that the first error is minimizedand the second error is maximized and a process of training thediscriminator such that a training data score, corresponding to themodified training data inputted into the discriminator or the modifiedobfuscated training data inputted into the discriminator, is maximizedand such that the obfuscated training data score is minimized; whereinthe learning network includes a first learning network to an n-thlearning network respectively having one or more first learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the process of (II), the processorperforms or supports another device to perform (i) a process ofinputting the compression adaptive obfuscated training data into each ofthe first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into each of the first learning network to the n-th learningnetwork, to thereby allow each of the first learning network to the n-thlearning network to (ii-1) apply its corresponding learning operation tothe training data by using respectively the first learned parameters tothe n-th learned parameters, and thus (ii-2) output each piece of(2_1)-st characteristic information for training to (2_n)-thcharacteristic information for training on the training data, andwherein, at the process of (III), the processor performs or supportsanother device to perform (i) a process of training the obfuscationnetwork such that the first error, which is an average over (i-1) atleast one (1_1)-st error calculated by referring to the (1_1)-stcharacteristic information for training and the (2_1)-st characteristicinformation for training to (i-2) at least one (1_n)-th error calculatedby referring to the (1_n)-th characteristic information for training andthe (2_n)-th characteristic information for training, is minimized andsuch that the second error, which is the obfuscated training data score,corresponding to the obfuscated training data inputted into thediscriminator or the compression adaptive obfuscated training datainputted into the discriminator, is maximized and (ii) a process oftraining the discriminator such that the training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized.
 19. A learning device for training an obfuscationnetwork to be used for obfuscating original data to protect personalinformation, comprising: at least one memory that stores instructions;and at least one processor configured to execute the instructions toperform or support another device to perform: (I) when training data isacquired, (i) a process of inputting the training data into anobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate obfuscated trainingdata, and (ii) a process of inputting the obfuscated training data intoa compression network, to thereby allow the compression network to(ii-1) compress the obfuscated training data and thus generate binarytraining data and (ii-2) decompress the binary training data and thusgenerate compression adaptive obfuscated training data, (II) (i) aprocess of inputting the compression adaptive obfuscated training datainto a learning network having one or more learned parameters, tothereby allow the learning network to (i-1) apply a learning operationto the compression adaptive obfuscated training data by using thelearned parameters and thus (i-2) generate first characteristicinformation for training corresponding to the compression adaptiveobfuscated training data and (ii) a process of inputting the trainingdata into the learning network, to thereby allow the learning network to(ii-1) apply the learning operation to the training data by using thelearned parameters and thus (ii-2) generate second characteristicinformation for training corresponding to the training data, and (III) aprocess of training the obfuscation network such that (i) at least onefirst error, calculated by referring to the first characteristicinformation for training and the second characteristic information fortraining, is minimized and (ii) at least one second error, calculated byreferring to (ii-1) (ii-1-a) modified training data, created bymodifying the training data, or modified obfuscated training data,created by modifying the obfuscated training data, and (ii-1-b) theobfuscated training data or (ii-2) (ii-2-a) the modified training dataor the modified obfuscated training data and (ii-2-b) the compressionadaptive obfuscated training data, is maximized; wherein the learningnetwork includes a first learning network to an n-th learning networkrespectively having one or more first learned parameters to one or moren-th learned parameters wherein n is an integer greater than 0; wherein,at the process of (III), on condition that an obfuscated training datascore, corresponding to the obfuscated training data inputted into adiscriminator capable of determining whether its inputted data is realor fake or the compression adaptive obfuscated training data inputtedinto the discriminator, has been acquired as the second error, theprocessor performs or supports another device to perform a process oftraining the obfuscation network such that the first error is minimizedand the second error is maximized and a process of training thediscriminator such that a training data score, corresponding to themodified training data inputted into the discriminator or the modifiedobfuscated training data inputted into the discriminator, is maximizedand such that the obfuscated training data score is minimized; whereinthe learning network includes a first learning network to an n-thlearning network respectively having one or more first learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the process of (I), the processorperforms or supports another device to perform (i) a process ofinputting the training data into the obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate first obfuscated training data and (ii) a process of inputtingthe first obfuscated training data into the compression network, tothereby allow the compression network to (ii-1) compress the firstobfuscated training data and thus generate first binary training dataand (ii-2) decompress the first binary training data and thus generatefirst compression adaptive obfuscated training data, wherein, at theprocess of (II), the processor performs or supports another device toperform (i) a process of inputting the first compression adaptiveobfuscated training data into the first learning network, to therebyallow the first learning network to (i-1) apply the learning operationto the first compression adaptive obfuscated training data by using thefirst learned parameters of the first learning network and thus (i-2)output (1_1)-st characteristic information for training on the firstcompression adaptive obfuscated training data and (ii) a process ofinputting the training data into the first learning network, to therebyallow the first learning network to (ii-1) apply the learning operationto the training data by using the first learned parameters and thus(ii-2) output (2_1)-st characteristic information for training on thetraining data, wherein, at the process of (III), the processor performsor supports another device to perform (i) a process of training theobfuscation network such that at least one (1_1)-st error, calculated byreferring to the (1_1)-st characteristic information for training andthe (2_1)-st characteristic information for training, is minimized andsuch that at least one (2_1)-st error, which is a first obfuscatedtraining data score, corresponding to the first obfuscated training datainputted into the discriminator or the first compression adaptiveobfuscated training data inputted into the discriminator, is maximized,to thereby allow the obfuscation network to be a first trainedobfuscation network and (ii) a process of training the discriminatorsuch that a first training data score, corresponding to the modifiedtraining data inputted into the discriminator or first modifiedobfuscated training data inputted into the discriminator, is maximizedand such that the first obfuscated training data score is minimized, tothereby allow the discriminator to be a first trained discriminator,wherein the first modified obfuscated training data is created bymodifying the first obfuscated training data and wherein, whileincreasing an integer k from 2 to n, the processor performs or supportsanother device to perform (i) a process of inputting the training datainto the (k-1)-th trained obfuscation network, to thereby allow the(k-1)-th trained obfuscation network to obfuscate the training data andthus to generate k-th obfuscated training data and a process ofinputting the k-th obfuscated training data into the compressionnetwork, to thereby allow the compression network to (1) compress thek-th obfuscated training data and thus generate k-th binary trainingdata and (2) decompress the k-th binary training data and thus generatek-th compression adaptive obfuscated training data, (ii) (ii-1) aprocess of inputting the k-th compression adaptive obfuscated trainingdata into a k-th learning network, to thereby allow the k-th learningnetwork to apply the learning operation to the k-th compression adaptiveobfuscated training data by using one or more k-th learned parameters ofthe k-th learning network and thus to output (1_k)-th characteristicinformation for training on the k-th compression adaptive obfuscatedtraining data and (ii-2) a process of inputting the training data intothe k-th learning network, to thereby allow the k-th learning network toapply the learning operation to the training data by using the k-thlearned parameters and thus to output (2_k)-th characteristicinformation for training on the training data, and (iii) (iii-1) aprocess of training the (k-1)-th trained obfuscation network such thatat least one (1_k)-th error, calculated by referring to the (1_k)-thcharacteristic information for training and the (2_k)-th characteristicinformation for training, is minimized and such that at least one(2_k)-th error, which is a k-th obfuscated training data score,corresponding to the k-th obfuscated training data inputted into a(k-1)-th trained discriminator or the k-th compression adaptiveobfuscated training data inputted into the (k-1)-th traineddiscriminator, is maximized, to thereby allow the (k-1)-th trainedobfuscation network to be a k-th trained obfuscation network and (iii-2)a process of training the (k-1)-th trained discriminator such that ak-th training data score, corresponding to the modified training datainputted into the (k-1)-th trained discriminator or k-th modifiedobfuscated training data inputted into the (k-1)-th traineddiscriminator, is maximized and such that the k-th obfuscated trainingdata is minimized, to thereby allow the (k-1)-th trained discriminatorto be a k-th trained discriminator, wherein the k-th modified obfuscatedtraining data is created by modifying the k-th obfuscated training data.20. A learning device for training an obfuscation network to be used forobfuscating original data to protect personal information, comprising:at least one memory that stores instructions; and at least one processorconfigured to execute the instructions to perform or support anotherdevice to perform: (I) when training data is acquired, (i) a process ofinputting the training data into an obfuscation network, to therebyallow the obfuscation network to obfuscate the training data and thus togenerate obfuscated training data, and (ii) a process of inputting theobfuscated training data into a compression network, to thereby allowthe compression network to (ii-1) compress the obfuscated training dataand thus generate binary training data and (ii-2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata, (II) (i) a process of inputting the compression adaptiveobfuscated training data into a learning network having one or morelearned parameters, to thereby allow the learning network to (i-1) applya learning operation to the compression adaptive obfuscated trainingdata by using the learned parameters and thus (i-2) generate firstcharacteristic information for training corresponding to the compressionadaptive obfuscated training data and (ii) a process of inputting thetraining data into the learning network, to thereby allow the learningnetwork to (ii-1) apply the learning operation to the training data byusing the learned parameters and thus (ii-2) generate secondcharacteristic information for training corresponding to the trainingdata, and (III) a process of training the obfuscation network such that(i) at least one first error, calculated by referring to the firstcharacteristic information for training and the second characteristicinformation for training, is minimized and (ii) at least one seconderror, calculated by referring to (ii-1) (ii-1-a) modified trainingdata, created by modifying the training data, or modified obfuscatedtraining data, created by modifying the obfuscated training data, and(ii-1-b) the obfuscated training data or (ii-2) (ii-2-a) the modifiedtraining data or the modified obfuscated training data and (ii-2-b) thecompression adaptive obfuscated training data, is maximized; wherein thelearning network includes a first learning network to an n-th learningnetwork respectively having one or more first learned parameters to oneor more n-th learned parameters wherein n is an integer greater than 0;wherein, at the process of (III), on condition that an obfuscatedtraining data score, corresponding to the obfuscated training datainputted into a discriminator capable of determining whether itsinputted data is real or fake or the compression adaptive obfuscatedtraining data inputted into the discriminator, has been acquired as thesecond error, the processor performs or supports another device toperform a process of training the obfuscation network such that thefirst error is minimized and the second error is maximized and a processof training the discriminator such that a training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized; wherein a maximum of the training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is 1 as a value for determining the modified trainingdata or the modified obfuscated training data as real and wherein aminimum of the obfuscated training data score, corresponding to theobfuscated training data inputted into the discriminator or thecompression adaptive obfuscated training data inputted into thediscriminator, is 0 as a value for determining the obfuscated trainingdata or the compression adaptive obfuscated training data as fake.
 21. Atesting device for testing an obfuscation network to be used forobfuscating original data to protect personal information, comprising:at least one memory that stores instructions; and at least one processorconfigured to execute the instructions to perform or support anotherdevice to perform: (I) on condition that the learning device hasperformed or supported another device to perform (i) a process ofinputting training data into an obfuscation network, to thereby allowthe obfuscation network to obfuscate the training data and thus togenerate obfuscated training data and a process of inputting theobfuscated training data into a compression network, to thereby allowthe compression network to (1) compress the obfuscated training data andthus generate binary training data and (2) decompress the binarytraining data and thus generate compression adaptive obfuscated trainingdata, (ii) (ii-1) a process of inputting the compression adaptiveobfuscated training data into a learning network having one or morelearned parameters, to thereby allow the learning network to apply alearning operation to the compression adaptive obfuscated training databy using the learned parameters and thus to output first characteristicinformation for training on the compression adaptive obfuscated trainingdata and (ii-2) a process of inputting the training data into thelearning network, to thereby allow the learning network to apply thelearning operation to the training data by using the learned parametersand thus to output second characteristic information for training on thetraining data, and (iii) a process of training the obfuscation networksuch that at least one first error, calculated by referring to the firstcharacteristic information for training and the second characteristicinformation for training, is minimized and such that at least one seconderror, which is calculated by referring to (iii-1) (iii-1-a) modifiedtraining data, created by modifying the training data, or modifiedobfuscated training data, created by modifying the obfuscated trainingdata, and (iii-1-b) the obfuscated training data or (iii-2) (iii-2-a)the modified training data or the modified obfuscated training data and(iii-2-b) the compression adaptive obfuscated training data, ismaximized, a process of acquiring test data, and (II) a process ofinputting the test data into the obfuscation network, to thereby allowthe obfuscation network to obfuscate the test data by using the learnedparameters of the obfuscation network and thus to output obfuscated testdata; wherein, at the process of (I), the learning network includes afirst learning network to an n-th learning network respectively havingone or more first parameters to one or more n-th learned parameterswherein n is an integer greater than 0, and wherein the learning devicehas performed or supported another device to perform (i) a process ofinputting the compression adaptive obfuscated training data into each ofthe first learning network to the n-th learning network, to therebyallow each of the first learning network to the n-th learning network to(i-1) apply its corresponding learning operation to the compressionadaptive obfuscated training data by using respectively the firstlearned parameters to the n-th learned parameters of the first learningnetwork to the n-th learning network, and thus (i-2) output each pieceof (1_1)-st characteristic information for training to (1_n)-thcharacteristic information for training on the compression adaptiveobfuscated training data, (ii) a process of inputting the training datainto each of the first learning network to the n-th learning network, tothereby allow each of the first learning network to the n-th learningnetwork to (ii-1) apply its corresponding learning operation to thetraining data by using respectively the first learned parameters to then-th learned parameters, and thus (ii-2) output each piece of (2_1)-stcharacteristic information for training to (2_n)-th characteristicinformation for training on the training data, and (iii) a process oftraining the obfuscation network such that the first error is minimizedwhich is an average over (iii-1) the (1_1)-st error acquired byreferring to the (1_1)-st characteristic information for training andthe (2_1)-st characteristic information for training to (iii-2) the(1_n)-th error acquired by referring to the (1_n)-th characteristicinformation for training and the (2_n)-th characteristic information fortraining and such that the second error is maximized which is calculatedby referring to (iii-3) (iii-3-a) the modified training data or themodified obfuscated training data and (iii-3-b) the obfuscated trainingdata or (iii-4) (iii-4-a) the modified training data or the modifiedobfuscated training data and (iii-4-b) the compression adaptiveobfuscated training data.
 22. A testing device for testing anobfuscation network to be used for obfuscating original data to protectpersonal information, comprising: at least one memory that storesinstructions; and at least one processor configured to execute theinstructions to perform or support another device to perform: (I) oncondition that the learning device has performed or supported anotherdevice to perform (i) a process of inputting training data into anobfuscation network, to thereby allow the obfuscation network toobfuscate the training data and thus to generate obfuscated trainingdata and a process of inputting the obfuscated training data into acompression network, to thereby allow the compression network to (1)compress the obfuscated training data and thus generate binary trainingdata and (2) decompress the binary training data and thus generatecompression adaptive obfuscated training data, (ii) (ii-1) a process ofinputting the compression adaptive obfuscated training data into alearning network having one or more learned parameters, to thereby allowthe learning network to apply a learning operation to the compressionadaptive obfuscated training data by using the learned parameters andthus to output first characteristic information for training on thecompression adaptive obfuscated training data and (ii-2) a process ofinputting the training data into the learning network, to thereby allowthe learning network to apply the learning operation to the trainingdata by using the learned parameters and thus to output secondcharacteristic information for training on the training data, and (iii)a process of training the obfuscation network such that at least onefirst error, calculated by referring to the first characteristicinformation for training and the second characteristic information fortraining, is minimized and such that at least one second error, which iscalculated by referring to (iii-1) (iii-1-a) modified training data,created by modifying the training data, or modified obfuscated trainingdata, created by modifying the obfuscated training data, and (iii-1-b)the obfuscated training data or (iii-2) (iii-2-a) the modified trainingdata or the modified obfuscated training data and (iii-2-b) thecompression adaptive obfuscated training data, is maximized, a processof acquiring test data, and (II) a process of inputting the test datainto the obfuscation network, to thereby allow the obfuscation networkto obfuscate the test data by using the learned parameters of theobfuscation network and thus to output obfuscated test data; wherein, atthe process of (I), upon acquiring an obfuscated training data score, asthe second error, corresponding to the obfuscated training data inputtedinto a discriminator capable of determining whether its inputted data isreal or fake or the compression adaptive obfuscated training datainputted into the discriminator, the learning device has performed orsupported another device to perform a process of training theobfuscation network such that the first error is minimized and thesecond error is maximized and a process of training the discriminatorsuch that a training data score, corresponding to the modified trainingdata inputted into the discriminator or the modified obfuscated trainingdata inputted into the discriminator, is maximized and such that theobfuscated training data score is minimized; wherein, at the process of(I), the learning network includes a 1-st learning network to an n-thlearning network respectively having one or more 1-st learned parametersto one or more n-th learned parameters wherein n is an integer greaterthan 0, and wherein the learning device has performed or supportedanother device to perform (i) a process of inputting the compressionadaptive obfuscated training data into each of the first learningnetwork to the n-th learning network, to thereby allow each of the firstlearning network to the n-th learning network to (i-1) apply itscorresponding learning operation to the compression adaptive obfuscatedtraining data by using respectively the first learned parameters to then-th learned parameters of the first learning network to the n-thlearning network, and thus (i-2) output each piece of (1_1)-stcharacteristic information for training to (1_n)-th characteristicinformation for training on the compression adaptive obfuscated trainingdata, (ii) a process of inputting the training data into each of thefirst learning network to the n-th learning network, to thereby alloweach of the first learning network to the n-th learning network to(ii-1) apply its corresponding learning operation to the training databy using respectively the first learned parameters to the n-th learnedparameters, and thus (ii-2) output each piece of (2_1)-st characteristicinformation for training to (2_n)-th characteristic information fortraining on the training data, and (iii) a process of training theobfuscation network such that the first error is minimized which is anaverage over (iii-1) the (1_1)-st error acquired by referring to the(1_1)-st characteristic information for training and the (2_1)-stcharacteristic information for training to (iii-2) the (1_n)-th erroracquired by referring to the (1_n)-th characteristic information fortraining and the (2_n)-th characteristic information for training andsuch that the second error which is the obfuscated training data score,corresponding to the obfuscated training data inputted into thediscriminator or the compression adaptive obfuscated training datainputted into the discriminator, is maximized and (iv) a process oftraining the discriminator such that the training data score,corresponding to the modified training data inputted into thediscriminator or the modified obfuscated training data inputted into thediscriminator, is maximized and such that the obfuscated training datascore is minimized.